完成训练模块的转移

application/urls.py

@@ -11,6 +11,7 @@ from apps.vadmin.system.views import app as vadmin_system_app
 from apps.vadmin.record.views import app as vadmin_record_app
 from apps.vadmin.help.views import app as vadmin_help_app
 from apps.business.project.views import app as project_app
+from apps.business.train.views import app as train_app
 
 
 # 引入应用中的路由
@@ -21,4 +22,5 @@ urlpatterns = [
     {"ApiRouter": vadmin_record_app, "prefix": "/vadmin/record", "tags": ["记录管理"]},
     {"ApiRouter": vadmin_help_app, "prefix": "/vadmin/help", "tags": ["帮助中心管理"]},
     {"ApiRouter": project_app, "prefix": "/business/project", "tags": ["项目管理"]},
+    {"ApiRouter": train_app, "prefix": "/business/train", "tags": ["训练管理"]},
 ]

apps/business/project/crud.py

@@ -5,23 +5,19 @@
 # @File           : crud.py
 # @IDE            : PyCharm
 # @desc           : 数据访问层
-import application.settings
 from . import schemas, models, params
 from apps.vadmin.auth.utils.validation.auth import Auth
 from utils import os_utils as os, random_utils as ru
 from utils.huawei_obs import ObsClient
 from utils import status
 from core.exception import CustomException
-if application.settings.DEBUG:
-    from application.config.development import datasets_url, runs_url, images_url
-else:
-    from application.config.production import datasets_url, runs_url, images_url
+from application.settings import datasets_url, runs_url, images_url
 
 from typing import Any, List
 from core.crud import DalBase
 from fastapi import UploadFile
 from sqlalchemy.ext.asyncio import AsyncSession
-from sqlalchemy import select, func, case, and_
+from sqlalchemy import select, func, case
 
 
 class ProjectInfoDal(DalBase):
@@ -96,6 +92,9 @@ class ProjectInfoDal(DalBase):
             project: schemas.ProjectInfoIn,
             auth: Auth
     ) -> Any:
+        """
+        新建项目
+        """
         obj = self.model(**project.model_dump())
         obj.user_id = auth.user.id
         obj.project_no = ru.random_str(6)
@@ -106,7 +105,9 @@ class ProjectInfoDal(DalBase):
             obj.dept_id = 0
         else:
             obj.dept_id = auth.dept_ids[0]
+        # 新建数据集文件夹
         os.create_folder(datasets_url, obj.project_no)
+        # 新建训练文件夹
         os.create_folder(runs_url, obj.project_no)
         await self.flush(obj)
         return await self.out_dict(obj, None, False, schemas.ProjectInfoOut)
@@ -214,6 +215,55 @@ class ProjectImageDal(DalBase):
         ObsClient.del_objects(object_keys)
         await self.delete_datas(ids)
 
+    async def get_img_count(
+            self,
+            proj_id: int) -> int:
+        """
+        查询图片数量
+        """
+        train_count = await self.get_count(
+            v_where=[models.ProjectImage.project_id == proj_id, models.ProjectImage.img_type == 'train'])
+        val_count = await self.get_count(
+            v_where=[models.ProjectImage.project_id == proj_id, models.ProjectImage.img_type == 'val'])
+        return train_count, val_count
+
+    async def check_image_label(
+            self,
+            proj_id: int) -> int:
+        """
+        查询图片未标注数量
+        """
+        # 1 子查询
+        subquery = (
+            select(
+                models.ProjectImgLabel.image_id,
+                func.ifnull(func.count(models.ProjectImgLabel.id), 0).label('label_count')
+            )
+            .group_by(models.ProjectImgLabel.image_id)
+            .subquery()
+        )
+        # 2 主查询
+        query = (
+            select(
+                models.ProjectImage,
+                func.ifnull(subquery.c.label_count, 0).label('label_count')
+            )
+            .outerjoin(subquery, models.ProjectImage.id == subquery.c.image_id)
+        )
+        train_count_sql = await self.filter_core(
+            v_start_sql=query,
+            v_where=[models.ProjectImage.project_id == proj_id, models.ProjectImage.img_type == 'train'],
+            v_return_sql=True)
+        train_count = await self.get_count(train_count_sql)
+
+        val_count_sql = await self.filter_core(
+            v_start_sql=query,
+            v_where=[models.ProjectImage.project_id == proj_id, models.ProjectImage.img_type == 'val'],
+            v_return_sql=True)
+        val_count = await self.get_count(val_count_sql)
+
+        return train_count, val_count
+
 
 class ProjectLabelDal(DalBase):
     """
@@ -233,14 +283,27 @@ class ProjectLabelDal(DalBase):
             label_id: int = None
     ):
         wheres = [
-            models.ProjectLabel.project_id == pro_id,
-            models.ProjectLabel.label_name == name
+            self.model.project_id == pro_id,
+            self.model.label_name == name
         ]
         if label_id:
-            wheres.append(models.ProjectLabel.id != label_id)
+            wheres.append(self.model.id != label_id)
         count = await self.get_count(v_where=wheres)
         return count > 0
 
+    async def get_label_for_train(self, project_id: int):
+        id_list = []
+        name_list = []
+        label_list = self.get_datas(
+            v_where=[self.model.project_id == project_id],
+            v_order='asc',
+            v_order_field='id',
+            v_return_count=False)
+        for label in label_list:
+            id_list.append(label.id)
+            name_list.append(label.label_name)
+        return id_list, name_list
+
 
 class ProjectImgLabelDal(DalBase):
     """
@@ -260,6 +323,13 @@ class ProjectImgLabelDal(DalBase):
             img.image_id = image_id
         await self.create_datas(img_labels)
 
+    async def get_img_label_list(self, image_id: int):
+        return await self.get_datas(
+            v_return_count=False,
+            v_where=[self.model.image_id == image_id],
+            v_order="asc",
+            v_order_field="id")
+
 
 class ProjectImgLeaferDal(DalBase):
     """

apps/business/project/params/project_info.py

@@ -15,14 +15,10 @@ class ProjectInfoParams(QueryParams):
             self,
             project_name: str | None = Query(None, title="项目名称"),
             type_code: str | None = Query(None, title="项目类别"),
-            dept_id: str | None = Query(None, title="部门id"),
-            user_id: str | None = Query(None, title="用户id"),
             params: Paging = Depends()
     ):
         super().__init__(params)
         self.project_name = ("like", project_name)
         self.type_code = type_code
-        self.dept_id = dept_id
-        self.user_id = user_id
 
 

apps/business/train/crud.py

@@ -9,6 +9,8 @@
 from sqlalchemy.ext.asyncio import AsyncSession
 from core.crud import DalBase
 from . import models, schemas
+from utils import os_utils as os
+from utils.csv_utils import read_csv
 
 
 class ProjectTrainDal(DalBase):
@@ -17,4 +19,75 @@ class ProjectTrainDal(DalBase):
         super(ProjectTrainDal, self).__init__()
         self.db = db
         self.model = models.ProjectTrain
-        self.schema = schemas.ProjectTrainSimpleOut
+        self.schema = schemas.ProjectTrainOut
+
+    async def get_result(self, train_id: int):
+        """
+        查询训练报告
+        """
+        data = await self.get_data(data_id=train_id)
+        if data is None:
+            return None
+        result_csv_path = os.file_path(data.train_url, 'results.csv')
+        result_row = read_csv(result_csv_path)
+        report_data = {}
+        # 轮数
+        epoch_data = []
+        # 边界框回归损失（Bounding Box Loss），衡量预测框位置（中心坐标、宽高）与真实框的差异，值越低表示定位越准。
+        train_box_loss = []
+        # 目标置信度损失（Objectness Loss），衡量检测到目标的置信度误差（即是否包含物体），值越低表示模型越能正确判断有无物体。
+        train_obj_loss = []
+        # 分类损失（Classification Loss），衡量预测类别与真实类别的差异，值越低表示分类越准。
+        train_cls_loss = []
+
+        # 验证集的边界框回归损失，反映模型在未见数据上的定位能力。
+        val_box_loss = []
+        # 验证集的目标置信度损失，反映模型在未见数据上判断物体存在的能力。
+        val_obj_loss = []
+        # 验证集的分类损失，反映模型在未见数据上的分类准确性。
+        val_cls_loss = []
+
+        # 精确率（Precision）：正确检测的正样本占所有预测为正样本的比例，反映“误检率”。值越高说明误检越少。
+        m_p = []
+        # 召回率（Recall）：正确检测的正样本占所有真实正样本的比例，反映“漏检率”。值越高说明漏检越少。
+        m_r = []
+
+        # 主干网络（Backbone）的学习率。
+        x_lr0 = []
+        # 检测头（Head）的学习率。
+        x_lr1 = []
+
+        for row in result_row:
+            epoch_data.append(row[0].strip())
+
+            train_box_loss.append(row[1].strip())
+            train_obj_loss.append(row[2].strip())
+            train_cls_loss.append(row[3].strip())
+
+            val_box_loss.append(row[8].strip())
+            val_obj_loss.append(row[9].strip())
+            val_cls_loss.append(row[10].strip())
+
+            m_p.append(row[4].strip())
+            m_r.append(row[5].strip())
+
+            x_lr0.append(row[11].strip())
+            x_lr1.append(row[12].strip())
+
+        report_data['epoch_data'] = epoch_data
+
+        report_data['train_box_loss'] = train_box_loss
+        report_data['train_obj_loss'] = train_obj_loss
+        report_data['train_cls_loss'] = train_cls_loss
+
+        report_data['val_box_loss'] = val_box_loss
+        report_data['val_obj_loss'] = val_obj_loss
+        report_data['val_cls_loss'] = val_cls_loss
+
+        report_data['m_p'] = m_p
+        report_data['m_r'] = m_r
+
+        report_data['x_lr0'] = x_lr0
+        report_data['x_lr1'] = x_lr1
+
+        return report_data

apps/business/train/models/__init__.py

@@ -0,0 +1 @@
+from .train import ProjectTrain

apps/business/train/models/train.py

@@ -6,7 +6,7 @@ from db.db_base import BaseModel
 
 class ProjectTrain(BaseModel):
     """
-    项目训练版本信息表
+    项目训练信息表
     """
     __tablename__ = "project_train"
     __table_args__ = ({'comment': '项目训练版本信息表'})

apps/business/train/params/project_train.py

@@ -6,10 +6,15 @@
 # @IDE            : PyCharm
 # @desc           : 项目巡逻片信息
 
-from fastapi import Depends
+from fastapi import Depends, Query
 from core.dependencies import Paging, QueryParams
 
 
 class ProjectTrainParams(QueryParams):
-    def __init__(self, params: Paging = Depends()):
+    def __init__(
+            self,
+            project_id: int | 0 = Query(0, title="项目id"),
+            params: Paging = Depends()
+    ):
         super().__init__(params)
+        self.project_id = project_id

apps/business/train/schemas/__init__.py

@@ -1 +1 @@
-from .project_train import ProjectTrain, ProjectTrainSimpleOut
+from .project_train import ProjectTrainIn, ProjectTrainOut

apps/business/train/schemas/project_train.py

@@ -6,27 +6,29 @@
 # @IDE            : PyCharm
 # @desc           : pydantic 模型，用于数据库序列化操作
 
+from datetime import datetime
 from pydantic import BaseModel, Field, ConfigDict
-from core.data_types import DatetimeStr
+from typing import Optional
+
+"""
+项目训练版本信息表
+"""
 
 
-class ProjectTrain(BaseModel):
-    project_id: int = Field(..., title="None")
-    train_version: str = Field(..., title="None")
-    train_url: str = Field(..., title="None")
-    train_data: str = Field(..., title="None")
-    weights_id: int = Field(..., title="None")
-    weights_name: str = Field(..., title="None")
-    epochs: int = Field(..., title="None")
-    patience: int = Field(..., title="None")
-    best_pt: str = Field(..., title="None")
-    last_pt: str = Field(..., title="None")
-    user_id: int = Field(..., title="None")
+class ProjectTrainIn(BaseModel):
+    project_id: Optional[int] = Field(..., description="项目id")
+    weights_id: Optional[str] = Field(None, description="权重文件")
+    epochs: Optional[int] = Field(50, description="训练轮数")
+    patience: Optional[int] = Field(20, description="早停的耐心值")
 
 
-class ProjectTrainSimpleOut(ProjectTrain):
+class ProjectTrainOut(BaseModel):
+    id: Optional[int] = Field(None, description="训练id")
+    train_version: Optional[str] = Field(None, description="训练版本号")
+    weights_name: Optional[str] = Field(None, description="权重名称")
+    epochs: Optional[int] = Field(None, description="训练轮数")
+    patience: Optional[int] = Field(None, description="早停的耐心值")
+    create_time: Optional[datetime] = Field(None, description="训练时间")
+
     model_config = ConfigDict(from_attributes=True)
 
-    id: int = Field(..., title="编号")
-    create_datetime: DatetimeStr = Field(..., title="创建时间")
-    update_datetime: DatetimeStr = Field(..., title="更新时间")

apps/business/train/service.py

@@ -0,0 +1,200 @@
+from . import schemas, models, crud
+from apps.business.project import schemas as proj_schemas, models as proj_models, crud as proj_crud
+from utils import os_utils as os
+from application.settings import *
+from utils.websocket_server import room_manager
+
+
+import yaml
+import asyncio
+import subprocess
+from typing import List
+from redis.asyncio import Redis
+from sqlalchemy.ext.asyncio import AsyncSession
+
+
+async def before_train(proj_info: proj_models.ProjectInfo, db: AsyncSession):
+    """
+    yolov5执行训练任务
+    :param proj_info: 项目信息
+    :param db: 数据库session
+    :return:
+    """
+    proj_dal = proj_crud.ProjectInfoDal(db)
+    img_dal = proj_crud.ProjectImageDal(db)
+    label_dal = proj_crud.ProjectLabelDal(db)
+    # 先查询两个图片列表
+    project_images_train = img_dal.get_data(
+        v_where=[proj_models.ProjectImage.project_id == proj_info.id, proj_models.ProjectImage.img_type == 'train'])
+    project_images_val = img_dal.get_data(
+        v_where=[proj_models.ProjectImage.project_id == proj_info.id, proj_models.ProjectImage.img_type == 'val'])
+
+    # 得到训练版本
+    version_path = 'v' + str(proj_info.train_version + 1)
+
+    # 创建训练的根目录
+    train_path = os.create_folder(datasets_url, proj_info.project_no, version_path)
+
+    # 查询项目所属标签，返回两个 id，name一一对应的数组
+    label_id_list, label_name_list = label_dal.get_label_for_train(proj_info.id)
+
+    # 创建图片的的两个文件夹
+    img_path_train = os.create_folder(train_path, 'images', 'train')
+    img_path_val = os.create_folder(train_path, 'images', 'val')
+
+    # 创建标签的两个文件夹
+    label_path_train = os.create_folder(train_path, 'labels', 'train')
+    label_path_val = os.create_folder(train_path, 'labels', 'val')
+
+    # 在根目录下创建yaml文件
+    yaml_file = os.file_path(train_path, proj_info.project_no + '.yaml')
+    yaml_data = {
+        'path': train_path,
+        'train': 'images/train',
+        'val': 'images/val',
+        'test': None,
+        'names': {i: name for i, name in enumerate(label_name_list)}
+    }
+    with open(yaml_file, 'w', encoding='utf-8') as file:
+        yaml.dump(yaml_data, file, allow_unicode=True, default_flow_style=False)
+
+    # 开始循环复制图片和生成label.txt
+    # 先操作train
+    operate_img_label(project_images_train, img_path_train, label_path_train, db, label_id_list)
+    # 再操作val
+    operate_img_label(project_images_val, img_path_val, label_path_val, db, label_id_list)
+
+    # 开始执行异步训练
+    data = yaml_file
+    project = os.file_path(runs_url, proj_info.project_no)
+    name = version_path
+
+    return data, project, name
+
+
+async def operate_img_label(
+        img_list: List[proj_models.ProjectImgLabel],
+        img_path: str,
+        label_path: str,
+        db: AsyncSession,
+        label_id_list: []):
+    """
+    生成图片和标签内容
+    :param label_id_list:
+    :param db: 数据库session
+    :param img_list:
+    :param img_path:
+    :param label_path:
+    :return:
+    """
+    for i in range(len(img_list)):
+        image = img_list[i]
+        # 先复制图片，并把图片改名，不改后缀
+        file_name = 'image' + str(i)
+        os.copy_and_rename_file(image.image_url, img_path, file_name)
+        # 查询这张图片的label信息然后生成这张照片的txt文件
+        img_label_list = await proj_crud.ProjectImgLabelDal(db).get_img_label_list(image.id)
+        label_txt_path = os.file_path(label_path, file_name + '.txt')
+        with open(label_txt_path, 'w', encoding='utf-8') as file:
+            for image_label in img_label_list:
+                index = label_id_list.index(image_label.label_id)
+                file.write(str(index) + ' ' + image_label.mark_center_x + ' '
+                           + image_label.mark_center_y + ' '
+                           + image_label.mark_width + ' '
+                           + image_label.mark_height + '\n')
+
+
+async def run_event_loop(
+        data: str,
+        project: str,
+        name: str,
+        train_in: schemas.ProjectTrainIn,
+        project_id: int,
+        db: AsyncSession):
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    # 运行异步函数
+    loop.run_until_complete(run_commend(data, project, name, train_in.epochs, train_in.patience, train_in.weights_id,
+                                        project_id, db))
+    # 可选: 关闭循环
+    loop.close()
+
+
+async def run_commend(
+        data: str,
+        project: str,
+        name: str,
+        epochs: int,
+        patience: int,
+        weights: str,
+        project_id: int,
+        db: AsyncSession,
+        rd: Redis):
+    """
+    执行训练
+    :param data: 训练数据集
+    :param project: 训练结果的项目目录
+    :param name: 实验名称
+    :param epochs: 训练轮数
+    :param patience: 早停耐心值
+    :param weights: 权重文件
+    :param project_id: 项目id
+    :param db: 数据库session
+    :param rd: redis连接
+    :return:
+    """
+    yolo_path = os.file_path(yolo_url, 'train.py')
+    room = 'train_' + str(project_id)
+    await room_manager.send_to_room(room, f"AiCheckV2.0: 模型训练开始，请稍等。。。\n")
+    commend = ["python", '-u', yolo_path, "--data=" + data, "--project=" + project, "--name=" + name,
+               "--epochs=" + str(epochs), "--batch-size=8", "--exist-ok", "--patience=" + str(patience)]
+
+    # 增加权重文件，在之前训练的基础上重新巡逻
+    if weights != '' and weights is not None:
+        train_info = await crud.ProjectTrainDal(db).get_data(data_id=int(weights))
+        if train_info is not None:
+            commend.append("--weights=" + train_info.best_pt)
+
+    is_gpu = rd.get('is_gpu')
+    # 判断是否存在cuda版本
+    if is_gpu == 'True':
+        commend.append("--device=0")
+    # 启动子进程
+    with subprocess.Popen(
+            commend,
+            bufsize=1,  # bufsize=0时，为不缓存；bufsize=1时，按行缓存；bufsize为其他正整数时，为按照近似该正整数的字节数缓存
+            shell=False,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,  # 这里可以显示yolov5训练过程中出现的进度条等信息
+            text=True,  # 缓存内容为文本，避免后续编码显示问题
+            encoding='utf-8',
+    ) as process:
+        while process.poll() is None:
+            line = process.stdout.readline()
+            process.stdout.flush()  # 刷新缓存，防止缓存过多造成卡死
+            if line != '\n' and '0%' not in line:
+                await room_manager.send_to_room(room, line + '\n')
+
+        # 等待进程结束并获取返回码
+        return_code = process.wait()
+        if return_code != 0:
+            await room_manager.send_to_room(room, 'error')
+        else:
+            await room_manager.send_to_room(room, 'success')
+            # 然后保存版本训练信息
+            train = models.ProjectTrain()
+            train.project_id = project_id
+            train.train_version = name
+            train_url = os.file_path(project, name)
+            train.train_url = train_url
+            train.train_data = data
+            bast_pt_path = os.file_path(train_url, 'weights', 'best.pt')
+            last_pt_path = os.file_path(train_url, 'weights', 'last.pt')
+            train.best_pt = bast_pt_path
+            train.last_pt = last_pt_path
+            if weights is not None and weights != '':
+                train.weights_id = weights
+                train.weights_name = train_info.train_version
+            train.patience = patience
+            train.epochs = epochs
+            await crud.ProjectTrainDal(db).create_data(data=train)

apps/business/train/views.py

@@ -3,49 +3,70 @@
 # @version        : 1.0
 # @Create Time    : 2025/04/03 10:32
 # @File           : views.py
-# @IDE            : PyCharm
-# @desc           : 路由，视图文件
-
-from sqlalchemy.ext.asyncio import AsyncSession
-from fastapi import APIRouter, Depends
-from . import models, schemas, crud, params
-from core.dependencies import IdList
+from . import models, schemas, crud
+from apps.business.project.crud import ProjectInfoDal, ProjectImageDal
+from utils.response import SuccessResponse, ErrorResponse
 from apps.vadmin.auth.utils.current import AllUserAuth
-from utils.response import SuccessResponse
 from apps.vadmin.auth.utils.validation.auth import Auth
-from core.database import db_getter
+import service
+
+import threading
+from fastapi import APIRouter, Depends
 
 
 app = APIRouter()
 
 
 ###########################################################
-#    项目巡逻片信息
+#    项目训练信息
 ###########################################################
-@app.get("/project/train", summary="获取项目巡逻片信息列表", tags=["项目巡逻片信息"])
-async def get_project_train_list(p: params.ProjectTrainParams = Depends(), auth: Auth = Depends(AllUserAuth())):
-    datas, count = await crud.ProjectTrainDal(auth.db).get_datas(**p.dict(), v_return_count=True)
-    return SuccessResponse(datas, count=count)
+@app.post("/", summary="执行训练")
+async def run_train(
+        train_in: schemas.ProjectTrainIn,
+        auth: Auth = Depends(AllUserAuth())):
+    proj_id = train_in.project_id
+    proj_dal = ProjectInfoDal(auth.db)
+    proj_img_dal = ProjectImageDal(auth.db)
+    proj_info = await proj_dal.get_data(proj_id)
+    if proj_info is None:
+        return ErrorResponse(msg="项目信息查询错误")
+    train_count, val_count = await proj_img_dal.get_img_count(proj_id)
+    if train_count == 0:
+        return ErrorResponse("请先上传训练图片")
+    if train_count < 10:
+        return ErrorResponse("训练图片少于10张，请继续上传训练图片")
+    if val_count == 0:
+        return ErrorResponse("请先上传验证图片")
+    if val_count < 5:
+        return ErrorResponse("验证图片少于5张，请继续上传验证图片")
+    train_label_count, val_label_count = await proj_img_dal.check_image_label(proj_id)
+    if train_label_count > 0:
+        return ErrorResponse("训练图片中存在未标注的图片")
+    if val_label_count > 0:
+        return ErrorResponse("验证图片中存在未标注的图片")
+    data, project, name = service.before_train(proj_info, auth.db)
+    # 异步执行操作，操作过程通过websocket进行同步
+    thread_train = threading.Thread(
+        target=service.run_event_loop,
+        args=(data, project, name, train_in, proj_id, auth.db,))
+    thread_train.start()
+    return SuccessResponse(msg="执行成功")
 
 
-@app.post("/project/train", summary="创建项目巡逻片信息", tags=["项目巡逻片信息"])
-async def create_project_train(data: schemas.ProjectTrain, auth: Auth = Depends(AllUserAuth())):
-    return SuccessResponse(await crud.ProjectTrainDal(auth.db).create_data(data=data))
+@app.get("/{proj_id}", summary="查询训练列表")
+async def train_list(
+        proj_id: int,
+        auth: Auth = Depends(AllUserAuth())):
+    datas = await crud.ProjectTrainDal(auth.db).get_datas(
+        v_where=[models.ProjectTrain.project_id == proj_id],
+        v_schema=schemas.ProjectTrainOut,
+        v_order="asc",
+        v_order_field="id",v_return_count=False)
+    return SuccessResponse(data=datas)
 
 
-@app.delete("/project/train", summary="删除项目巡逻片信息", description="硬删除", tags=["项目巡逻片信息"])
-async def delete_project_train_list(ids: IdList = Depends(), auth: Auth = Depends(AllUserAuth())):
-    await crud.ProjectTrainDal(auth.db).delete_datas(ids=ids.ids, v_soft=False)
-    return SuccessResponse("删除成功")
-
-
-@app.put("/project/train/{data_id}", summary="更新项目巡逻片信息", tags=["项目巡逻片信息"])
-async def put_project_train(data_id: int, data: schemas.ProjectTrain, auth: Auth = Depends(AllUserAuth())):
-    return SuccessResponse(await crud.ProjectTrainDal(auth.db).put_data(data_id, data))
-
-
-@app.get("/project/train/{data_id}", summary="获取项目巡逻片信息信息", tags=["项目巡逻片信息"])
-async def get_project_train(data_id: int, db: AsyncSession = Depends(db_getter)):
-    schema = schemas.ProjectTrainSimpleOut
-    return SuccessResponse(await crud.ProjectTrainDal(db).get_data(data_id, v_schema=schema))
+@app.get("/result/{proj_id}", summary="查询训练报告")
+async def get_result(train_id:int, auth: Auth = Depends(AllUserAuth())):
+    result = await crud.ProjectTrainDal(auth.db).get_result(train_id)
+    return SuccessResponse(data=result)
 

core/event.py

@@ -6,7 +6,7 @@
 # @IDE            : PyCharm
 # @desc           : 全局事件
 
-
+import torch
 from fastapi import FastAPI
 from motor.motor_asyncio import AsyncIOMotorClient
 from application.settings import REDIS_DB_URL, MONGO_DB_URL, MONGO_DB_NAME, EVENTS
@@ -68,6 +68,9 @@ async def connect_redis(app: FastAPI, status: bool):
             response = await rd.ping()
             if response:
                 print("Redis 连接成功")
+                # 数据初始化
+                is_gpu = torch.cuda.is_available()
+                rd.set('is_gpu', str(is_gpu))
             else:
                 print("Redis 连接失败")
         except AuthenticationError as e:

core/websocket_app.py

@@ -0,0 +1,30 @@
+from fastapi import WebSocket
+from fastapi import FastAPI
+from starlette.websockets import WebSocketState
+
+from utils.websocket_server import room_manager
+
+
+def websocket_config(app: FastAPI):
+
+    @app.websocket("/{room}")
+    async def websocket_room(websocket: WebSocket, room: str):
+        """
+        websocket 房间管理
+        :param websocket:
+        :param room:
+        :return:
+        """
+        await websocket.accept()
+        await room_manager.add_to_room(room, websocket)
+        try:
+            while True:
+                data = await websocket.receive_text()
+                await room_manager.broadcast_to_room(room, data, exclude_websocket=websocket)
+        except Exception as e:
+            if websocket.client_state != WebSocketState.DISCONNECTED:
+                await websocket.close(code=1000)
+        finally:
+            await room_manager.remove_from_room(room, websocket)
+            if websocket.client_state != WebSocketState.DISCONNECTED:
+                await websocket.close(code=1001)

deep_sort/__init__.py

@@ -0,0 +1,19 @@
+from .deep_sort import DeepSort
+
+__all__ = ['DeepSort', 'build_tracker']
+
+
+def build_tracker(cfg, use_cuda):
+    if cfg.USE_FASTREID:
+        return DeepSort(model_path=cfg.FASTREID.CHECKPOINT, model_config=cfg.FASTREID.CFG,
+                        max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
+                        nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
+                        max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
+                        use_cuda=use_cuda)
+
+    else:
+        return DeepSort(model_path=cfg.DEEPSORT.REID_CKPT,
+                        max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
+                        nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
+                        max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
+                        use_cuda=use_cuda)

deep_sort/configs/deep_sort.yaml

@@ -0,0 +1,10 @@
+DEEPSORT:
+  REID_CKPT: "./deep_sort/deep/checkpoint/ckpt.t7"
+  MAX_DIST: 0.2
+  MIN_CONFIDENCE: 0.5
+  NMS_MAX_OVERLAP: 0.5
+  MAX_IOU_DISTANCE: 0.7
+  MAX_AGE: 70
+  N_INIT: 3
+  NN_BUDGET: 100
+  

deep_sort/configs/fastreid.yaml

@@ -0,0 +1,3 @@
+FASTREID:
+  CFG: "thirdparty/fast-reid/configs/Market1501/bagtricks_R50.yml"
+  CHECKPOINT: "deep_sort/deep/checkpoint/market_bot_R50.pth"

deep_sort/configs/mask_rcnn.yaml

@@ -0,0 +1,6 @@
+MASKRCNN:
+  LABEL: "./coco_classes.json"
+  WEIGHT: "./detector/Mask_RCNN/save_weights/maskrcnn_resnet50_fpn_coco.pth"
+
+  NUM_CLASSES: 90
+  BOX_THRESH: 0.5

deep_sort/configs/mmdet.yaml

@@ -0,0 +1,5 @@
+MMDET:
+  CFG: "thirdparty/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py"
+  CHECKPOINT: "detector/MMDet/weight/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth"
+
+  SCORE_THRESH: 0.5

deep_sort/deep/GETTING_STARTED.md

@@ -0,0 +1,82 @@
+In deepsort algorithm, appearance feature extraction network used to extract features from **image_crops** for matching purpose.The original model used in paper is in `model.py`, and its parameter here [ckpt.t7](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6). This repository also provides a `resnet.py` script and its pre-training weights on Imagenet here.
+
+```
+# resnet18
+https://download.pytorch.org/models/resnet18-5c106cde.pth
+# resnet34    
+https://download.pytorch.org/models/resnet34-333f7ec4.pth
+# resnet50
+https://download.pytorch.org/models/resnet50-19c8e357.pth
+# resnext50_32x4d
+https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
+```
+
+## Dataset PrePare
+
+To train the model, first you need download [Market1501](http://www.liangzheng.com.cn/Project/project_reid.html) dataset or [Mars](http://www.liangzheng.com.cn/Project/project_mars.html) dataset.  
+
+If you want to train on your **own dataset**, assuming you have already downloaded the dataset.The dataset should be arranged in the following way.
+
+```
+ ├── dataset_root: The root dir of the dataset.
+  ├── class1: Category 1 is located in the folder dir.
+   ├── xxx1.jpg: Image belonging to category 1.
+   ├── xxx2.jpg: Image belonging to category 1.
+  ├── class2: Category 2 is located in the folder dir.
+   ├── xxx3.jpg: Image belonging to category 2.
+   ├── xxx4.jpg: Image belonging to category 2.
+  ├── class3: Category 3 is located in the folder dir.
+  ...
+  ...
+```
+
+## Training the RE-ID model
+
+Assuming you have already prepare the dataset. Then you can use the following command to start your training progress.
+
+#### training on a single GPU
+
+```python
+usage: train.py [--data-dir]
+                [--epochs]
+                [--batch_size]
+                [--lr]
+                [--lrf]
+                [--weights]
+                [--freeze-layers]
+                [--gpu_id]
+
+# default use cuda:0, use Net in `model.py`
+python train.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path]
+# you can use `--freeze-layers` option to freeze full convolutional layer parameters except fc layers parameters
+python train.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path] --freeze-layers
+```
+
+#### training on multiple GPU
+
+```python
+usage: train_multiGPU.py [--data-dir]
+                         [--epochs]
+                         [--batch_size]
+                         [--lr]
+                         [--lrf]
+                         [--syncBN]
+                         [--weights]
+                         [--freeze-layers]
+                         # not change the following parameters, the system will automatically assignment
+                         [--device]
+                         [--world_size]
+                         [--dist_url]
+                         
+# default use cuda:0, cuda:1, cuda:2, cuda:3, use resnet18 in `resnet.py`
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 train_multiGPU.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path] 
+# you can use `--freeze-layers` option to freeze full convolutional layer parameters except fc layers parameters
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 train_multiGPU.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path] --freeze-layers
+```
+
+An example of training progress is as follows:
+
+![train.jpg](./train.jpg)
+
+The last, you can evaluate it using [test.py](deep_sort/deep/test.py) and [evaluate.py](deep_sort/deep/evalute.py).
+

deep_sort/deep/datasets.py

@@ -0,0 +1,92 @@
+import json
+import os
+import random
+
+import cv2
+from PIL import Image
+import torch
+from torch.utils.data import Dataset
+import matplotlib.pyplot as plt
+
+
+class ClsDataset(Dataset):
+    def __init__(self, images_path, images_labels, transform=None):
+        self.images_path = images_path
+        self.images_labels = images_labels
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.images_path)
+
+    def __getitem__(self, idx):
+        img = cv2.imread(self.images_path[idx])
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = Image.fromarray(img)
+        label = self.images_labels[idx]
+
+        if self.transform is not None:
+            img = self.transform(img)
+        return img, label
+
+    @staticmethod
+    def collate_fn(batch):
+        images, labels = tuple(zip(*batch))
+        images = torch.stack(images, dim=0)
+        labels = torch.as_tensor(labels)
+        return images, labels
+
+
+def read_split_data(root, valid_rate=0.2):
+    assert os.path.exists(root), 'dataset root: {} does not exist.'.format(root)
+
+    class_names = [cls for cls in os.listdir(root) if os.path.isdir(os.path.join(root, cls))]
+    class_names.sort()
+
+    class_indices = {name: i for i, name in enumerate(class_names)}
+    json_str = json.dumps({v: k for k, v in class_indices.items()}, indent=4)
+    with open('class_indices.json', 'w') as f:
+        f.write(json_str)
+
+    train_images_path = []
+    train_labels = []
+    val_images_path = []
+    val_labels = []
+    per_class_num = []
+
+    supported = ['.jpg', '.JPG', '.png', '.PNG']
+    for cls in class_names:
+        cls_path = os.path.join(root, cls)
+        images_path = [os.path.join(cls_path, i) for i in os.listdir(cls_path)
+                       if os.path.splitext(i)[-1] in supported]
+        images_label = class_indices[cls]
+        per_class_num.append(len(images_path))
+
+        val_path = random.sample(images_path, int(len(images_path) * valid_rate))
+        for img_path in images_path:
+            if img_path in val_path:
+                val_images_path.append(img_path)
+                val_labels.append(images_label)
+            else:
+                train_images_path.append(img_path)
+                train_labels.append(images_label)
+
+    print("{} images were found in the dataset.".format(sum(per_class_num)))
+    print("{} images for training.".format(len(train_images_path)))
+    print("{} images for validation.".format(len(val_images_path)))
+
+    assert len(train_images_path) > 0, "number of training images must greater than zero"
+    assert len(val_images_path) > 0, "number of validation images must greater than zero"
+
+    plot_distribution = False
+    if plot_distribution:
+        plt.bar(range(len(class_names)), per_class_num, align='center')
+        plt.xticks(range(len(class_names)), class_names)
+
+        for i, v in enumerate(per_class_num):
+            plt.text(x=i, y=v + 5, s=str(v), ha='center')
+
+        plt.xlabel('classes')
+        plt.ylabel('numbers')
+        plt.title('the distribution of dataset')
+        plt.show()
+    return [train_images_path, train_labels], [val_images_path, val_labels], len(class_names)

deep_sort/deep/evaluate.py

@@ -0,0 +1,15 @@
+import torch
+
+features = torch.load("features.pth")
+qf = features["qf"]
+ql = features["ql"]
+gf = features["gf"]
+gl = features["gl"]
+
+scores = qf.mm(gf.t())
+res = scores.topk(5, dim=1)[1][:,0]
+top1correct = gl[res].eq(ql).sum().item()
+
+print("Acc top1:{:.3f}".format(top1correct/ql.size(0)))
+
+

deep_sort/deep/feature_extractor.py

@@ -0,0 +1,93 @@
+import torch
+import torchvision.transforms as transforms
+import numpy as np
+import cv2
+import logging
+
+from .model import Net
+from .resnet import resnet18
+# from fastreid.config import get_cfg
+# from fastreid.engine import DefaultTrainer
+# from fastreid.utils.checkpoint import Checkpointer
+
+
+class Extractor(object):
+    def __init__(self, model_path, use_cuda=True):
+        self.net = Net(reid=True)
+        # self.net = resnet18(reid=True)
+        self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
+        state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)
+        self.net.load_state_dict(state_dict if 'net_dict' not in state_dict else state_dict['net_dict'], strict=False)
+        logger = logging.getLogger("root.tracker")
+        logger.info("Loading weights from {}... Done!".format(model_path))
+        self.net.to(self.device)
+        self.size = (64, 128)
+        self.norm = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ])
+
+    def _preprocess(self, im_crops):
+        """
+        TODO:
+            1. to float with scale from 0 to 1
+            2. resize to (64, 128) as Market1501 dataset did
+            3. concatenate to a numpy array
+            3. to torch Tensor
+            4. normalize
+        """
+
+        def _resize(im, size):
+            return cv2.resize(im.astype(np.float32) / 255., size)
+
+        im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float()
+        return im_batch
+
+    def __call__(self, im_crops):
+        im_batch = self._preprocess(im_crops)
+        with torch.no_grad():
+            im_batch = im_batch.to(self.device)
+            features = self.net(im_batch)
+        return features.cpu().numpy()
+
+
+class FastReIDExtractor(object):
+    def __init__(self, model_config, model_path, use_cuda=True):
+        cfg = get_cfg()
+        cfg.merge_from_file(model_config)
+        cfg.MODEL.BACKBONE.PRETRAIN = False
+        self.net = DefaultTrainer.build_model(cfg)
+        self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
+
+        Checkpointer(self.net).load(model_path)
+        logger = logging.getLogger("root.tracker")
+        logger.info("Loading weights from {}... Done!".format(model_path))
+        self.net.to(self.device)
+        self.net.eval()
+        height, width = cfg.INPUT.SIZE_TEST
+        self.size = (width, height)
+        self.norm = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ])
+
+    def _preprocess(self, im_crops):
+        def _resize(im, size):
+            return cv2.resize(im.astype(np.float32) / 255., size)
+
+        im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float()
+        return im_batch
+
+    def __call__(self, im_crops):
+        im_batch = self._preprocess(im_crops)
+        with torch.no_grad():
+            im_batch = im_batch.to(self.device)
+            features = self.net(im_batch)
+        return features.cpu().numpy()
+
+
+if __name__ == '__main__':
+    img = cv2.imread("demo.jpg")[:, :, (2, 1, 0)]
+    extr = Extractor("checkpoint/ckpt.t7")
+    feature = extr(img)
+    print(feature.shape)

deep_sort/deep/model.py

@@ -0,0 +1,105 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, c_in, c_out, is_downsample=False):
+        super(BasicBlock, self).__init__()
+        self.is_downsample = is_downsample
+        if is_downsample:
+            self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=2, padding=1, bias=False)
+        else:
+            self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(c_out)
+        self.relu = nn.ReLU(True)
+        self.conv2 = nn.Conv2d(c_out, c_out, 3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(c_out)
+        if is_downsample:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(c_in, c_out, 1, stride=2, bias=False),
+                nn.BatchNorm2d(c_out)
+            )
+        elif c_in != c_out:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(c_in, c_out, 1, stride=1, bias=False),
+                nn.BatchNorm2d(c_out)
+            )
+            self.is_downsample = True
+
+    def forward(self, x):
+        y = self.conv1(x)
+        y = self.bn1(y)
+        y = self.relu(y)
+        y = self.conv2(y)
+        y = self.bn2(y)
+        if self.is_downsample:
+            x = self.downsample(x)
+        return F.relu(x.add(y), True)
+
+
+def make_layers(c_in, c_out, repeat_times, is_downsample=False):
+    blocks = []
+    for i in range(repeat_times):
+        if i == 0:
+            blocks += [BasicBlock(c_in, c_out, is_downsample=is_downsample), ]
+        else:
+            blocks += [BasicBlock(c_out, c_out), ]
+    return nn.Sequential(*blocks)
+
+
+class Net(nn.Module):
+    def __init__(self, num_classes=751, reid=False):
+        super(Net, self).__init__()
+        # 3 128 64
+        self.conv = nn.Sequential(
+            nn.Conv2d(3, 64, 3, stride=1, padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+            # nn.Conv2d(32,32,3,stride=1,padding=1),
+            # nn.BatchNorm2d(32),
+            # nn.ReLU(inplace=True),
+            nn.MaxPool2d(3, 2, padding=1),
+        )
+        # 32 64 32
+        self.layer1 = make_layers(64, 64, 2, False)
+        # 32 64 32
+        self.layer2 = make_layers(64, 128, 2, True)
+        # 64 32 16
+        self.layer3 = make_layers(128, 256, 2, True)
+        # 128 16 8
+        self.layer4 = make_layers(256, 512, 2, True)
+        # 256 8 4
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+        # 256 1 1 
+        self.reid = reid
+        self.classifier = nn.Sequential(
+            nn.Linear(512, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(inplace=True),
+            nn.Dropout(),
+            nn.Linear(256, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        # B x 128
+        if self.reid:
+            x = x.div(x.norm(p=2, dim=1, keepdim=True))
+            return x
+        # classifier
+        x = self.classifier(x)
+        return x
+
+
+if __name__ == '__main__':
+    net = Net()
+    x = torch.randn(4, 3, 128, 64)
+    y = net(x)
+

deep_sort/deep/multi_train_utils/distributed_utils.py

@@ -0,0 +1,67 @@
+import os
+
+import torch
+import torch.distributed as dist
+
+
+def init_distributed_mode(args):
+    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
+        args.rank = int(os.environ['RANK'])
+        args.world_size = int(os.environ['WORLD_SIZE'])
+        args.gpu = int(os.environ['LOCAL_RANK'])
+    elif 'SLURM_PROCID' in os.environ:
+        args.rank = int(os.environ['SLURM_PROCID'])
+        args.gpu = args.rank % torch.cuda.device_count()
+    else:
+        print("Not using distributed mode")
+        args.distributed = False
+        return
+
+    args.distributed = True
+
+    torch.cuda.set_device(args.gpu)
+    args.dist_backend = 'nccl'
+    print('| distributed init (rank {}): {}'.format(args.rank, args.dist_url), flush=True)
+    dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
+                            world_size=args.world_size, rank=args.rank)
+    dist.barrier()
+
+
+def cleanup():
+    dist.destroy_process_group()
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def reduce_value(value, average=True):
+    world_size = get_world_size()
+    if world_size < 2:
+        return value
+    with torch.no_grad():
+        dist.all_reduce(value)
+        if average:
+            value /= world_size
+
+        return value

deep_sort/deep/multi_train_utils/train_eval_utils.py

@@ -0,0 +1,90 @@
+import sys
+
+from tqdm import tqdm
+import torch
+
+from .distributed_utils import reduce_value, is_main_process
+
+
+def load_model(state_dict, model_state_dict, model):
+    for k in state_dict:
+        if k in model_state_dict:
+            if state_dict[k].shape != model_state_dict[k].shape:
+                print('Skip loading parameter {}, required shape {}, ' \
+                      'loaded shape {}.'.format(
+                    k, model_state_dict[k].shape, state_dict[k].shape))
+                state_dict[k] = model_state_dict[k]
+        else:
+            print('Drop parameter {}.'.format(k))
+    for k in model_state_dict:
+        if not (k in state_dict):
+            print('No param {}.'.format(k))
+            state_dict[k] = model_state_dict[k]
+    model.load_state_dict(state_dict, strict=False)
+    return model
+
+
+def train_one_epoch(model, optimizer, data_loader, device, epoch):
+    model.train()
+    criterion = torch.nn.CrossEntropyLoss()
+    mean_loss = torch.zeros(1).to(device)
+    sum_num = torch.zeros(1).to(device)
+    optimizer.zero_grad()
+
+    if is_main_process():
+        data_loader = tqdm(data_loader, file=sys.stdout)
+
+    for idx, (images, labels) in enumerate(data_loader):
+        # forward
+        images, labels = images.to(device), labels.to(device)
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+
+        # backward
+        loss.backward()
+        loss = reduce_value(loss, average=True)
+        mean_loss = (mean_loss * idx + loss.detach()) / (idx + 1)
+        pred = torch.max(outputs, dim=1)[1]
+        sum_num += torch.eq(pred, labels).sum()
+
+        if is_main_process():
+            data_loader.desc = '[epoch {}] mean loss {}'.format(epoch, mean_loss.item())
+
+        if not torch.isfinite(loss):
+            print('loss is infinite, ending training')
+            sys.exit(1)
+
+        optimizer.step()
+        optimizer.zero_grad()
+    if device != torch.device('cpu'):
+        torch.cuda.synchronize(device)
+    sum_num = reduce_value(sum_num, average=False)
+
+    return sum_num.item(), mean_loss.item()
+
+
+@torch.no_grad()
+def evaluate(model, data_loader, device):
+    model.eval()
+    criterion = torch.nn.CrossEntropyLoss()
+    test_loss = torch.zeros(1).to(device)
+    sum_num = torch.zeros(1).to(device)
+    if is_main_process():
+        data_loader = tqdm(data_loader, file=sys.stdout)
+
+    for idx, (inputs, labels) in enumerate(data_loader):
+        inputs, labels = inputs.to(device), labels.to(device)
+        outputs = model(inputs)
+        loss = criterion(outputs, labels)
+        loss = reduce_value(loss, average=True)
+
+        test_loss = (test_loss * idx + loss.detach()) / (idx + 1)
+        pred = torch.max(outputs, dim=1)[1]
+        sum_num += torch.eq(pred, labels).sum()
+
+    if device != torch.device('cpu'):
+        torch.cuda.synchronize(device)
+
+    sum_num = reduce_value(sum_num, average=False)
+
+    return sum_num.item(), test_loss.item()

deep_sort/deep/resnet.py

@@ -0,0 +1,173 @@
+import torch.nn as nn
+import torch
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=3,
+                               stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channel)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channel)
+        self.downsample = downsample
+
+    def forward(self, x):
+        identity = x
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        out += identity
+        out = self.relu(out)
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_channel, out_channel, stride=1, downsample=None,
+                 groups=1, width_per_group=64):
+        super(Bottleneck, self).__init__()
+        width = int(out_channel * (width_per_group / 64.)) * groups
+
+        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width, kernel_size=1,
+                               stride=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(width)
+        self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, kernel_size=3,
+                               stride=stride, padding=1, bias=False, groups=groups)
+        self.bn2 = nn.BatchNorm2d(width)
+        self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel * self.expansion,
+                               kernel_size=1, stride=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(out_channel * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+
+    def forward(self, x):
+        identity = x
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, blocks_num, reid=False, num_classes=1000, groups=1, width_per_group=64):
+        super(ResNet, self).__init__()
+        self.reid = reid
+        self.in_channel = 64
+
+        self.groups = groups
+        self.width_per_group = width_per_group
+
+        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
+                               padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(self.in_channel)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layers(block, 64, blocks_num[0])
+        self.layer2 = self._make_layers(block, 128, blocks_num[1], stride=2)
+        self.layer3 = self._make_layers(block, 256, blocks_num[2], stride=2)
+        # self.layer4 = self._make_layers(block, 512, blocks_num[3], stride=1)
+
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(256 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layers(self, block, channel, block_num, stride=1):
+        downsample = None
+        if stride != 1 or self.in_channel != channel * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(channel * block.expansion)
+            )
+        layers = []
+        layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride,
+                            groups=self.groups, width_per_group=self.width_per_group))
+        self.in_channel = channel * block.expansion
+
+        for _ in range(1, block_num):
+            layers.append(block(self.in_channel, channel, groups=self.groups, width_per_group=self.width_per_group))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        # x = self.layer4(x)
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+
+        # B x 512
+        if self.reid:
+            x = x.div(x.norm(p=2, dim=1, keepdim=True))
+            return x
+        # classifier
+        x = self.fc(x)
+        return x
+
+
+def resnet18(num_classes=1000, reid=False):
+    # https://download.pytorch.org/models/resnet18-5c106cde.pth
+    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, reid=reid)
+
+
+def resnet34(num_classes=1000, reid=False):
+    # https://download.pytorch.org/models/resnet34-333f7ec4.pth
+    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, reid=reid)
+
+
+def resnet50(num_classes=1000, reid=False):
+    # https://download.pytorch.org/models/resnet50-19c8e357.pth
+    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, reid=reid)
+
+
+def resnext50_32x4d(num_classes=1000, reid=False):
+    # https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
+    groups = 32
+    width_per_group = 4
+    return ResNet(Bottleneck, [3, 4, 6, 3], reid=reid,
+                  num_classes=num_classes, groups=groups, width_per_group=width_per_group)
+
+
+if __name__ == '__main__':
+    net = resnet18(reid=True)
+    x = torch.randn(4, 3, 128, 64)
+    y = net(x)

deep_sort/deep/test.py

@@ -0,0 +1,77 @@
+import torch
+import torch.backends.cudnn as cudnn
+import torchvision
+
+import argparse
+import os
+
+from model import Net
+
+parser = argparse.ArgumentParser(description="Train on market1501")
+parser.add_argument("--data-dir", default='data', type=str)
+parser.add_argument("--no-cuda", action="store_true")
+parser.add_argument("--gpu-id", default=0, type=int)
+args = parser.parse_args()
+
+# device
+device = "cuda:{}".format(args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu"
+if torch.cuda.is_available() and not args.no_cuda:
+    cudnn.benchmark = True
+
+# data loader
+root = args.data_dir
+query_dir = os.path.join(root, "query")
+gallery_dir = os.path.join(root, "gallery")
+transform = torchvision.transforms.Compose([
+    torchvision.transforms.Resize((128, 64)),
+    torchvision.transforms.ToTensor(),
+    torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+queryloader = torch.utils.data.DataLoader(
+    torchvision.datasets.ImageFolder(query_dir, transform=transform),
+    batch_size=64, shuffle=False
+)
+galleryloader = torch.utils.data.DataLoader(
+    torchvision.datas0ets.ImageFolder(gallery_dir, transform=transform),
+    batch_size=64, shuffle=False
+)
+
+# net definition
+net = Net(reid=True)
+assert os.path.isfile("./checkpoint/ckpt.t7"), "Error: no checkpoint file found!"
+print('Loading from checkpoint/ckpt.t7')
+checkpoint = torch.load("./checkpoint/ckpt.t7")
+net_dict = checkpoint['net_dict']
+net.load_state_dict(net_dict, strict=False)
+net.eval()
+net.to(device)
+
+# compute features
+query_features = torch.tensor([]).float()
+query_labels = torch.tensor([]).long()
+gallery_features = torch.tensor([]).float()
+gallery_labels = torch.tensor([]).long()
+
+with torch.no_grad():
+    for idx, (inputs, labels) in enumerate(queryloader):
+        inputs = inputs.to(device)
+        features = net(inputs).cpu()
+        query_features = torch.cat((query_features, features), dim=0)
+        query_labels = torch.cat((query_labels, labels))
+
+    for idx, (inputs, labels) in enumerate(galleryloader):
+        inputs = inputs.to(device)
+        features = net(inputs).cpu()
+        gallery_features = torch.cat((gallery_features, features), dim=0)
+        gallery_labels = torch.cat((gallery_labels, labels))
+
+gallery_labels -= 2
+
+# save features
+features = {
+    "qf": query_features,
+    "ql": query_labels,
+    "gf": gallery_features,
+    "gl": gallery_labels
+}
+torch.save(features, "features.pth")

deep_sort/deep/train.py

@@ -0,0 +1,151 @@
+import argparse
+import os
+import tempfile
+
+import math
+import warnings
+import matplotlib.pyplot as plt
+import torch
+import torchvision
+from torch.optim import lr_scheduler
+
+from multi_train_utils.distributed_utils import init_distributed_mode, cleanup
+from multi_train_utils.train_eval_utils import train_one_epoch, evaluate, load_model
+import torch.distributed as dist
+from datasets import ClsDataset, read_split_data
+
+from model import Net
+from resnet import resnet18
+
+# plot figure
+x_epoch = []
+record = {'train_loss': [], 'train_err': [], 'test_loss': [], 'test_err': []}
+fig = plt.figure()
+ax0 = fig.add_subplot(121, title="loss")
+ax1 = fig.add_subplot(122, title="top1_err")
+
+
+def draw_curve(epoch, train_loss, train_err, test_loss, test_err):
+    global record
+    record['train_loss'].append(train_loss)
+    record['train_err'].append(train_err)
+    record['test_loss'].append(test_loss)
+    record['test_err'].append(test_err)
+
+    x_epoch.append(epoch)
+    ax0.plot(x_epoch, record['train_loss'], 'bo-', label='train')
+    ax0.plot(x_epoch, record['test_loss'], 'ro-', label='val')
+    ax1.plot(x_epoch, record['train_err'], 'bo-', label='train')
+    ax1.plot(x_epoch, record['test_err'], 'ro-', label='val')
+    if epoch == 0:
+        ax0.legend()
+        ax1.legend()
+    fig.savefig("train.jpg")
+
+
+def main(args):
+    batch_size = args.batch_size
+    device = 'cuda:{}'.format(args.gpu_id) if torch.cuda.is_available() else 'cpu'
+
+    train_info, val_info, num_classes = read_split_data(args.data_dir, valid_rate=0.2)
+    train_images_path, train_labels = train_info
+    val_images_path, val_labels = val_info
+
+    transform_train = torchvision.transforms.Compose([
+        torchvision.transforms.RandomCrop((128, 64), padding=4),
+        torchvision.transforms.RandomHorizontalFlip(),
+        torchvision.transforms.ToTensor(),
+        torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    transform_val = torchvision.transforms.Compose([
+        torchvision.transforms.Resize((128, 64)),
+        torchvision.transforms.ToTensor(),
+        torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    train_dataset = ClsDataset(
+        images_path=train_images_path,
+        images_labels=train_labels,
+        transform=transform_train
+    )
+    val_dataset = ClsDataset(
+        images_path=val_images_path,
+        images_labels=val_labels,
+        transform=transform_val
+    )
+
+    number_workers = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])
+    print('Using {} dataloader workers every process'.format(number_workers))
+
+    train_loader = torch.utils.data.DataLoader(
+        train_dataset,
+        batch_size=batch_size,
+        shuffle=True,
+        pin_memory=True,
+        num_workers=number_workers
+    )
+    val_loader = torch.utils.data.DataLoader(
+        val_dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        pin_memory=True,
+        num_workers=number_workers,
+    )
+
+    # net definition
+    start_epoch = 0
+    net = Net(num_classes=num_classes)
+    if args.weights:
+        print('Loading from ', args.weights)
+        checkpoint = torch.load(args.weights, map_location='cpu')
+        net_dict = checkpoint if 'net_dict' not in checkpoint else checkpoint['net_dict']
+        start_epoch = checkpoint['epoch'] if 'epoch' in checkpoint else start_epoch
+        net = load_model(net_dict, net.state_dict(), net)
+
+    if args.freeze_layers:
+        for name, param in net.named_parameters():
+            if 'classifier' not in name:
+                param.requires_grad = False
+
+    net.to(device)
+
+    # loss and optimizer
+    pg = [p for p in net.parameters() if p.requires_grad]
+    optimizer = torch.optim.SGD(pg, args.lr, momentum=0.9, weight_decay=5e-4)
+
+    lr = lambda x: ((1 + math.cos(x * math.pi / args.epochs)) / 2) * (1 - args.lrf) + args.lrf
+    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lr)
+    for epoch in range(start_epoch, start_epoch + args.epochs):
+        train_positive, train_loss = train_one_epoch(net, optimizer, train_loader, device, epoch)
+        train_acc = train_positive / len(train_dataset)
+        scheduler.step()
+
+        test_positive, test_loss = evaluate(net, val_loader, device)
+        test_acc = test_positive / len(val_dataset)
+
+        print('[epoch {}] accuracy: {}'.format(epoch, test_acc))
+
+        state_dict = {
+            'net_dict': net.state_dict(),
+            'acc': test_acc,
+            'epoch': epoch
+        }
+        torch.save(state_dict, './checkpoint/model_{}.pth'.format(epoch))
+        draw_curve(epoch, train_loss, 1 - train_acc, test_loss, 1 - test_acc)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Train on market1501")
+    parser.add_argument("--data-dir", default='data', type=str)
+    parser.add_argument('--epochs', type=int, default=40)
+    parser.add_argument('--batch_size', type=int, default=32)
+    parser.add_argument("--lr", default=0.001, type=float)
+    parser.add_argument('--lrf', default=0.1, type=float)
+
+    parser.add_argument('--weights', type=str, default='./checkpoint/resnet18.pth')
+    parser.add_argument('--freeze-layers', action='store_true')
+
+    parser.add_argument('--gpu_id', default='0', help='gpu id')
+    args = parser.parse_args()
+
+    main(args)

deep_sort/deep/train_multiGPU.py

@@ -0,0 +1,189 @@
+import argparse
+import os
+import tempfile
+
+import math
+import warnings
+import matplotlib.pyplot as plt
+import torch
+import torchvision
+from torch.optim import lr_scheduler
+
+from multi_train_utils.distributed_utils import init_distributed_mode, cleanup
+from multi_train_utils.train_eval_utils import train_one_epoch, evaluate, load_model
+import torch.distributed as dist
+from datasets import ClsDataset, read_split_data
+
+from resnet import resnet18
+
+
+# plot figure
+x_epoch = []
+record = {'train_loss': [], 'train_err': [], 'test_loss': [], 'test_err': []}
+fig = plt.figure()
+ax0 = fig.add_subplot(121, title="loss")
+ax1 = fig.add_subplot(122, title="top1_err")
+
+
+def draw_curve(epoch, train_loss, train_err, test_loss, test_err):
+    global record
+    record['train_loss'].append(train_loss)
+    record['train_err'].append(train_err)
+    record['test_loss'].append(test_loss)
+    record['test_err'].append(test_err)
+
+    x_epoch.append(epoch)
+    ax0.plot(x_epoch, record['train_loss'], 'bo-', label='train')
+    ax0.plot(x_epoch, record['test_loss'], 'ro-', label='val')
+    ax1.plot(x_epoch, record['train_err'], 'bo-', label='train')
+    ax1.plot(x_epoch, record['test_err'], 'ro-', label='val')
+    if epoch == 0:
+        ax0.legend()
+        ax1.legend()
+    fig.savefig("train.jpg")
+
+
+def main(args):
+    init_distributed_mode(args)
+
+    rank = args.rank
+    device = torch.device(args.device)
+    batch_size = args.batch_size
+    weights_path = args.weights
+    args.lr *= args.world_size
+    checkpoint_path = ''
+
+    if rank == 0:
+        print(args)
+        if os.path.exists('./checkpoint') is False:
+            os.mkdir('./checkpoint')
+
+    train_info, val_info, num_classes = read_split_data(args.data_dir, valid_rate=0.2)
+    train_images_path, train_labels = train_info
+    val_images_path, val_labels = val_info
+
+    transform_train = torchvision.transforms.Compose([
+        torchvision.transforms.RandomCrop((128, 64), padding=4),
+        torchvision.transforms.RandomHorizontalFlip(),
+        torchvision.transforms.ToTensor(),
+        torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    transform_val = torchvision.transforms.Compose([
+        torchvision.transforms.Resize((128, 64)),
+        torchvision.transforms.ToTensor(),
+        torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    train_dataset = ClsDataset(
+        images_path=train_images_path,
+        images_labels=train_labels,
+        transform=transform_train
+    )
+    val_dataset = ClsDataset(
+        images_path=val_images_path,
+        images_labels=val_labels,
+        transform=transform_val
+    )
+    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
+    val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
+
+    train_batch_sampler = torch.utils.data.BatchSampler(train_sampler, batch_size, drop_last=True)
+
+    number_workers = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])
+
+    if rank == 0:
+        print('Using {} dataloader workers every process'.format(number_workers))
+
+    train_loader = torch.utils.data.DataLoader(
+        train_dataset,
+        batch_sampler=train_batch_sampler,
+        pin_memory=True,
+        num_workers=number_workers
+    )
+    val_loader = torch.utils.data.DataLoader(
+        val_dataset,
+        sampler=val_sampler,
+        batch_size=batch_size,
+        pin_memory=True,
+        num_workers=number_workers,
+    )
+
+    # net definition
+    start_epoch = 0
+    net = resnet18(num_classes=num_classes)
+    if args.weights:
+        print('Loading from ', args.weights)
+        checkpoint = torch.load(args.weights, map_location='cpu')
+        net_dict = checkpoint if 'net_dict' not in checkpoint else checkpoint['net_dict']
+        start_epoch = checkpoint['epoch'] if 'epoch' in checkpoint else start_epoch
+        net = load_model(net_dict, net.state_dict(), net)
+    else:
+        warnings.warn("better providing pretraining weights")
+        checkpoint_path = os.path.join(tempfile.gettempdir(), 'initial_weights.pth')
+        if rank == 0:
+            torch.save(net.state_dict(), checkpoint_path)
+
+        dist.barrier()
+        net.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
+
+    if args.freeze_layers:
+        for name, param in net.named_parameters():
+            if 'fc' not in name:
+                param.requires_grad = False
+    else:
+        if args.syncBN:
+            net = torch.nn.SyncBatchNorm.convert_sync_batchnorm(net)
+    net.to(device)
+
+    net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[args.gpu])
+
+    # loss and optimizer
+    pg = [p for p in net.parameters() if p.requires_grad]
+    optimizer = torch.optim.SGD(pg, args.lr, momentum=0.9, weight_decay=5e-4)
+
+    lr = lambda x: ((1 + math.cos(x * math.pi / args.epochs)) / 2) * (1 - args.lrf) + args.lrf
+    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lr)
+    for epoch in range(start_epoch, start_epoch + args.epochs):
+        train_positive, train_loss = train_one_epoch(net, optimizer, train_loader, device, epoch)
+        train_acc = train_positive / len(train_dataset)
+        scheduler.step()
+
+        test_positive, test_loss = evaluate(net, val_loader, device)
+        test_acc = test_positive / len(val_dataset)
+
+        if rank == 0:
+            print('[epoch {}] accuracy: {}'.format(epoch, test_acc))
+
+            state_dict = {
+                'net_dict': net.module.state_dict(),
+                'acc': test_acc,
+                'epoch': epoch
+            }
+            torch.save(state_dict, './checkpoint/model_{}.pth'.format(epoch))
+        draw_curve(epoch, train_loss, 1 - train_acc, test_loss, 1 - test_acc)
+
+    if rank == 0:
+        if os.path.exists(checkpoint_path) is True:
+            os.remove(checkpoint_path)
+    cleanup()
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Train on market1501")
+    parser.add_argument("--data-dir", default='data', type=str)
+    parser.add_argument('--epochs', type=int, default=40)
+    parser.add_argument('--batch_size', type=int, default=32)
+    parser.add_argument("--lr", default=0.001, type=float)
+    parser.add_argument('--lrf', default=0.1, type=float)
+    parser.add_argument('--syncBN', type=bool, default=True)
+
+    parser.add_argument('--weights', type=str, default='./checkpoint/resnet18.pth')
+    parser.add_argument('--freeze-layers', action='store_true')
+
+    # not change the following parameters, the system will automatically assignment
+    parser.add_argument('--device', default='cuda', help='device id (i.e. 0 or 0, 1 or cpu)')
+    parser.add_argument('--world_size', default=4, type=int, help='number of distributed processes')
+    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
+    args = parser.parse_args()
+
+    main(args)

deep_sort/deep_sort.py

@@ -0,0 +1,121 @@
+import numpy as np
+import torch
+
+from .deep.feature_extractor import Extractor, FastReIDExtractor
+from .sort.nn_matching import NearestNeighborDistanceMetric
+from .sort.preprocessing import non_max_suppression
+from .sort.detection import Detection
+from .sort.tracker import Tracker
+
+__all__ = ['DeepSort']
+
+
+class DeepSort(object):
+    def __init__(self, model_path, model_config=None, max_dist=0.2, min_confidence=0.3, nms_max_overlap=1.0,
+                 max_iou_distance=0.7, max_age=70, n_init=3, nn_budget=100, use_cuda=True):
+        self.min_confidence = min_confidence
+        self.nms_max_overlap = nms_max_overlap
+
+        if model_config is None:
+            self.extractor = Extractor(model_path, use_cuda=use_cuda)
+        else:
+            self.extractor = FastReIDExtractor(model_config, model_path, use_cuda=use_cuda)
+
+        max_cosine_distance = max_dist
+        metric = NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
+        self.tracker = Tracker(metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init)
+
+    def update(self, bbox_xywh, confidences, classes, ori_img, masks=None):
+        self.height, self.width = ori_img.shape[:2]
+        # generate detections
+        features = self._get_features(bbox_xywh, ori_img)
+        bbox_tlwh = self._xywh_to_tlwh(bbox_xywh)
+        detections = [Detection(bbox_tlwh[i], conf, label, features[i], None if masks is None else masks[i])
+                      for i, (conf, label) in enumerate(zip(confidences, classes))
+                      if conf > self.min_confidence]
+
+        # run on non-maximum supression
+        boxes = np.array([d.tlwh for d in detections])
+        scores = np.array([d.confidence for d in detections])
+        indices = non_max_suppression(boxes, self.nms_max_overlap, scores)
+        detections = [detections[i] for i in indices]
+
+        # update tracker
+        self.tracker.predict()
+        self.tracker.update(detections)
+
+        # output bbox identities
+        outputs = []
+        mask_outputs = []
+        for track in self.tracker.tracks:
+            if not track.is_confirmed() or track.time_since_update > 1:
+                continue
+            box = track.to_tlwh()
+            x1, y1, x2, y2 = self._tlwh_to_xyxy(box)
+            track_id = track.track_id
+            track_cls = track.cls
+            outputs.append(np.array([x1, y1, x2, y2, track_cls, track_id], dtype=np.int32))
+            if track.mask is not None:
+                mask_outputs.append(track.mask)
+        if len(outputs) > 0:
+            outputs = np.stack(outputs, axis=0)
+        return outputs, mask_outputs
+
+    """
+    TODO:
+        Convert bbox from xc_yc_w_h to xtl_ytl_w_h
+    Thanks JieChen91@github.com for reporting this bug!
+    """
+
+    @staticmethod
+    def _xywh_to_tlwh(bbox_xywh):
+        if isinstance(bbox_xywh, np.ndarray):
+            bbox_tlwh = bbox_xywh.copy()
+        elif isinstance(bbox_xywh, torch.Tensor):
+            bbox_tlwh = bbox_xywh.clone()
+        bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2] / 2.
+        bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3] / 2.
+        return bbox_tlwh
+
+    def _xywh_to_xyxy(self, bbox_xywh):
+        x, y, w, h = bbox_xywh
+        x1 = max(int(x - w / 2), 0)
+        x2 = min(int(x + w / 2), self.width - 1)
+        y1 = max(int(y - h / 2), 0)
+        y2 = min(int(y + h / 2), self.height - 1)
+        return x1, y1, x2, y2
+
+    def _tlwh_to_xyxy(self, bbox_tlwh):
+        """
+        TODO:
+            Convert bbox from xtl_ytl_w_h to xc_yc_w_h
+        Thanks JieChen91@github.com for reporting this bug!
+        """
+        x, y, w, h = bbox_tlwh
+        x1 = max(int(x), 0)
+        x2 = min(int(x + w), self.width - 1)
+        y1 = max(int(y), 0)
+        y2 = min(int(y + h), self.height - 1)
+        return x1, y1, x2, y2
+
+    @staticmethod
+    def _xyxy_to_tlwh(bbox_xyxy):
+        x1, y1, x2, y2 = bbox_xyxy
+
+        t = x1
+        l = y1
+        w = int(x2 - x1)
+        h = int(y2 - y1)
+        return t, l, w, h
+
+    def _get_features(self, bbox_xywh, ori_img):
+        im_crops = []
+        for box in bbox_xywh:
+            x1, y1, x2, y2 = self._xywh_to_xyxy(box)
+            im = ori_img[y1:y2, x1:x2]
+            im_crops.append(im)
+        if im_crops:
+            features = self.extractor(im_crops)
+        else:
+            features = np.array([])
+        return features

deep_sort/sort/detection.py

@@ -0,0 +1,51 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+
+
+class Detection(object):
+    """
+    This class represents a bounding box detection in a single image.
+
+    Parameters
+    ----------
+    tlwh : array_like
+        Bounding box in format `(x, y, w, h)`.
+    confidence : float
+        Detector confidence score.
+    feature : array_like
+        A feature vector that describes the object contained in this image.
+
+    Attributes
+    ----------
+    tlwh : ndarray
+        Bounding box in format `(top left x, top left y, width, height)`.
+    confidence : ndarray
+        Detector confidence score.
+    feature : ndarray | NoneType
+        A feature vector that describes the object contained in this image.
+
+    """
+
+    def __init__(self, tlwh, confidence, label, feature, mask=None):
+        self.tlwh = np.asarray(tlwh, dtype=np.float32)
+        self.confidence = float(confidence)
+        self.cls = int(label)
+        self.feature = np.asarray(feature, dtype=np.float32)
+        self.mask = mask
+
+    def to_tlbr(self):
+        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
+        `(top left, bottom right)`.
+        """
+        ret = self.tlwh.copy()
+        ret[2:] += ret[:2]
+        return ret
+
+    def to_xyah(self):
+        """Convert bounding box to format `(center x, center y, aspect ratio,
+        height)`, where the aspect ratio is `width / height`.
+        """
+        ret = self.tlwh.copy()
+        ret[:2] += ret[2:] / 2
+        ret[2] /= ret[3]
+        return ret

deep_sort/sort/iou_matching.py

@@ -0,0 +1,81 @@
+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from . import linear_assignment
+
+
+def iou(bbox, candidates):
+    """Computer intersection over union.
+
+    Parameters
+    ----------
+    bbox : ndarray
+        A bounding box in format `(top left x, top left y, width, height)`.
+    candidates : ndarray
+        A matrix of candidate bounding boxes (one per row) in the same format
+        as `bbox`.
+
+    Returns
+    -------
+    ndarray
+        The intersection over union in [0, 1] between the `bbox` and each
+        candidate. A higher score means a larger fraction of the `bbox` is
+        occluded by the candidate.
+
+    """
+    bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]
+    candidates_tl = candidates[:, :2]
+    candidates_br = candidates[:, :2] + candidates[:, 2:]
+
+    tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis],
+               np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]]
+    br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis],
+               np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]]
+    wh = np.maximum(0., br - tl)
+
+    area_intersection = wh.prod(axis=1)
+    area_bbox = bbox[2:].prod()
+    area_candidates = candidates[:, 2:].prod(axis=1)
+    return area_intersection / (area_bbox + area_candidates - area_intersection)
+
+
+def iou_cost(tracks, detections, track_indices=None,
+             detection_indices=None):
+    """An intersection over union distance metric.
+
+    Parameters
+    ----------
+    tracks : List[deep_sort.track.Track]
+        A list of tracks.
+    detections : List[deep_sort.detection.Detection]
+        A list of detections.
+    track_indices : Optional[List[int]]
+        A list of indices to tracks that should be matched. Defaults to
+        all `tracks`.
+    detection_indices : Optional[List[int]]
+        A list of indices to detections that should be matched. Defaults
+        to all `detections`.
+
+    Returns
+    -------
+    ndarray
+        Returns a cost matrix of shape
+        len(track_indices), len(detection_indices) where entry (i, j) is
+        `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`.
+
+    """
+    if track_indices is None:
+        track_indices = np.arange(len(tracks))
+    if detection_indices is None:
+        detection_indices = np.arange(len(detections))
+
+    cost_matrix = np.zeros((len(track_indices), len(detection_indices)))
+    for row, track_idx in enumerate(track_indices):
+        if tracks[track_idx].time_since_update > 1:
+            cost_matrix[row, :] = linear_assignment.INFTY_COST
+            continue
+
+        bbox = tracks[track_idx].to_tlwh()
+        candidates = np.asarray([detections[i].tlwh for i in detection_indices])
+        cost_matrix[row, :] = 1. - iou(bbox, candidates)
+    return cost_matrix

deep_sort/sort/kalman_filter.py

@@ -0,0 +1,231 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+import scipy.linalg
+
+
+"""
+Table for the 0.95 quantile of the chi-square distribution with N degrees of
+freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv
+function and used as Mahalanobis gating threshold.
+"""
+chi2inv95 = {
+    1: 3.8415,
+    2: 5.9915,
+    3: 7.8147,
+    4: 9.4877,
+    5: 11.070,
+    6: 12.592,
+    7: 14.067,
+    8: 15.507,
+    9: 16.919}
+
+
+class KalmanFilter(object):
+    """
+    A simple Kalman filter for tracking bounding boxes in image space.
+
+    The 8-dimensional state space
+
+        x, y, a, h, vx, vy, va, vh
+
+    contains the bounding box center position (x, y), aspect ratio a, height h,
+    and their respective velocities.
+
+    Object motion follows a constant velocity model. The bounding box location
+    (x, y, a, h) is taken as direct observation of the state space (linear
+    observation model).
+
+    """
+
+    def __init__(self):
+        ndim, dt = 4, 1.
+
+        # Create Kalman filter model matrices.
+        self._motion_mat = np.eye(2 * ndim, 2 * ndim)
+        for i in range(ndim):
+            self._motion_mat[i, ndim + i] = dt
+        self._update_mat = np.eye(ndim, 2 * ndim)
+
+        # Motion and observation uncertainty are chosen relative to the current
+        # state estimate. These weights control the amount of uncertainty in
+        # the model. This is a bit hacky.
+        self._std_weight_position = 1. / 20
+        self._std_weight_velocity = 1. / 160
+
+    def initiate(self, measurement):
+        """Create track from unassociated measurement.
+
+        Parameters
+        ----------
+        measurement : ndarray
+            Bounding box coordinates (x, y, a, h) with center position (x, y),
+            aspect ratio a, and height h.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector (8 dimensional) and covariance matrix (8x8
+            dimensional) of the new track. Unobserved velocities are initialized
+            to 0 mean.
+
+        """
+        mean_pos = measurement
+        mean_vel = np.zeros_like(mean_pos)
+        mean = np.r_[mean_pos, mean_vel]
+
+        std = [
+            2 * self._std_weight_position * measurement[3],
+            2 * self._std_weight_position * measurement[3],
+            1e-2,
+            2 * self._std_weight_position * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            10 * self._std_weight_velocity * measurement[3],
+            1e-5,
+            10 * self._std_weight_velocity * measurement[3]]
+        covariance = np.diag(np.square(std))
+        return mean, covariance
+
+    def predict(self, mean, covariance):
+        """Run Kalman filter prediction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The 8 dimensional mean vector of the object state at the previous
+            time step.
+        covariance : ndarray
+            The 8x8 dimensional covariance matrix of the object state at the
+            previous time step.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the mean vector and covariance matrix of the predicted
+            state. Unobserved velocities are initialized to 0 mean.
+
+        """
+        std_pos = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-2,
+            self._std_weight_position * mean[3]]
+        std_vel = [
+            self._std_weight_velocity * mean[3],
+            self._std_weight_velocity * mean[3],
+            1e-5,
+            self._std_weight_velocity * mean[3]]
+        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
+
+        mean = np.dot(self._motion_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
+
+        return mean, covariance
+
+    def project(self, mean, covariance):
+        """Project state distribution to measurement space.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The state's mean vector (8 dimensional array).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the projected mean and covariance matrix of the given state
+            estimate.
+
+        """
+        std = [
+            self._std_weight_position * mean[3],
+            self._std_weight_position * mean[3],
+            1e-1,
+            self._std_weight_position * mean[3]]
+        innovation_cov = np.diag(np.square(std))
+
+        mean = np.dot(self._update_mat, mean)
+        covariance = np.linalg.multi_dot((
+            self._update_mat, covariance, self._update_mat.T))
+        return mean, covariance + innovation_cov
+
+    def update(self, mean, covariance, measurement):
+        """Run Kalman filter correction step.
+
+        Parameters
+        ----------
+        mean : ndarray
+            The predicted state's mean vector (8 dimensional).
+        covariance : ndarray
+            The state's covariance matrix (8x8 dimensional).
+        measurement : ndarray
+            The 4 dimensional measurement vector (x, y, a, h), where (x, y)
+            is the center position, a the aspect ratio, and h the height of the
+            bounding box.
+
+        Returns
+        -------
+        (ndarray, ndarray)
+            Returns the measurement-corrected state distribution.
+
+        """
+        projected_mean, projected_cov = self.project(mean, covariance)
+
+        chol_factor, lower = scipy.linalg.cho_factor(
+            projected_cov, lower=True, check_finite=False)
+        kalman_gain = scipy.linalg.cho_solve(
+            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
+            check_finite=False).T
+        innovation = measurement - projected_mean
+
+        new_mean = mean + np.dot(innovation, kalman_gain.T)
+        # new_covariance = covariance - np.linalg.multi_dot((
+        #     kalman_gain, projected_cov, kalman_gain.T))
+        new_covariance = covariance - np.linalg.multi_dot((
+            kalman_gain, self._update_mat, covariance))
+        return new_mean, new_covariance
+
+    def gating_distance(self, mean, covariance, measurements,
+                        only_position=False):
+        """Compute gating distance between state distribution and measurements.
+
+        A suitable distance threshold can be obtained from `chi2inv95`. If
+        `only_position` is False, the chi-square distribution has 4 degrees of
+        freedom, otherwise 2.
+
+        Parameters
+        ----------
+        mean : ndarray
+            Mean vector over the state distribution (8 dimensional).
+        covariance : ndarray
+            Covariance of the state distribution (8x8 dimensional).
+        measurements : ndarray
+            An Nx4 dimensional matrix of N measurements, each in
+            format (x, y, a, h) where (x, y) is the bounding box center
+            position, a the aspect ratio, and h the height.
+        only_position : Optional[bool]
+            If True, distance computation is done with respect to the bounding
+            box center position only.
+
+        Returns
+        -------
+        ndarray
+            Returns an array of length N, where the i-th element contains the
+            squared Mahalanobis distance between (mean, covariance) and
+            `measurements[i]`.
+
+        """
+        mean, covariance = self.project(mean, covariance)
+        if only_position:
+            mean, covariance = mean[:2], covariance[:2, :2]
+            measurements = measurements[:, :2]
+
+        cholesky_factor = np.linalg.cholesky(covariance)
+        d = measurements - mean
+        z = scipy.linalg.solve_triangular(
+            cholesky_factor, d.T, lower=True, check_finite=False,
+            overwrite_b=True)
+        squared_maha = np.sum(z * z, axis=0)
+        return squared_maha

deep_sort/sort/linear_assignment.py

@@ -0,0 +1,192 @@
+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+# from sklearn.utils.linear_assignment_ import linear_assignment
+from scipy.optimize import linear_sum_assignment as linear_assignment
+from . import kalman_filter
+
+
+INFTY_COST = 1e+5
+
+
+def min_cost_matching(
+        distance_metric, max_distance, tracks, detections, track_indices=None,
+        detection_indices=None):
+    """Solve linear assignment problem.
+
+    Parameters
+    ----------
+    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
+        The distance metric is given a list of tracks and detections as well as
+        a list of N track indices and M detection indices. The metric should
+        return the NxM dimensional cost matrix, where element (i, j) is the
+        association cost between the i-th track in the given track indices and
+        the j-th detection in the given detection_indices.
+    max_distance : float
+        Gating threshold. Associations with cost larger than this value are
+        disregarded.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : List[int]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above).
+    detection_indices : List[int]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above).
+
+    Returns
+    -------
+    (List[(int, int)], List[int], List[int])
+        Returns a tuple with the following three entries:
+        * A list of matched track and detection indices.
+        * A list of unmatched track indices.
+        * A list of unmatched detection indices.
+
+    """
+    if track_indices is None:
+        track_indices = np.arange(len(tracks))
+    if detection_indices is None:
+        detection_indices = np.arange(len(detections))
+
+    if len(detection_indices) == 0 or len(track_indices) == 0:
+        return [], track_indices, detection_indices  # Nothing to match.
+
+    cost_matrix = distance_metric(
+        tracks, detections, track_indices, detection_indices)
+    cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5
+
+    row_indices, col_indices = linear_assignment(cost_matrix)
+
+    matches, unmatched_tracks, unmatched_detections = [], [], []
+    for col, detection_idx in enumerate(detection_indices):
+        if col not in col_indices:
+            unmatched_detections.append(detection_idx)
+    for row, track_idx in enumerate(track_indices):
+        if row not in row_indices:
+            unmatched_tracks.append(track_idx)
+    for row, col in zip(row_indices, col_indices):
+        track_idx = track_indices[row]
+        detection_idx = detection_indices[col]
+        if cost_matrix[row, col] > max_distance:
+            unmatched_tracks.append(track_idx)
+            unmatched_detections.append(detection_idx)
+        else:
+            matches.append((track_idx, detection_idx))
+    return matches, unmatched_tracks, unmatched_detections
+
+
+def matching_cascade(
+        distance_metric, max_distance, cascade_depth, tracks, detections,
+        track_indices=None, detection_indices=None):
+    """Run matching cascade.
+
+    Parameters
+    ----------
+    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
+        The distance metric is given a list of tracks and detections as well as
+        a list of N track indices and M detection indices. The metric should
+        return the NxM dimensional cost matrix, where element (i, j) is the
+        association cost between the i-th track in the given track indices and
+        the j-th detection in the given detection indices.
+    max_distance : float
+        Gating threshold. Associations with cost larger than this value are
+        disregarded.
+    cascade_depth: int
+        The cascade depth, should be se to the maximum track age.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : Optional[List[int]]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above). Defaults to all tracks.
+    detection_indices : Optional[List[int]]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above). Defaults to all
+        detections.
+
+    Returns
+    -------
+    (List[(int, int)], List[int], List[int])
+        Returns a tuple with the following three entries:
+        * A list of matched track and detection indices.
+        * A list of unmatched track indices.
+        * A list of unmatched detection indices.
+
+    """
+    if track_indices is None:
+        track_indices = list(range(len(tracks)))
+    if detection_indices is None:
+        detection_indices = list(range(len(detections)))
+
+    unmatched_detections = detection_indices
+    matches = []
+    for level in range(cascade_depth):
+        if len(unmatched_detections) == 0:  # No detections left
+            break
+
+        track_indices_l = [
+            k for k in track_indices
+            if tracks[k].time_since_update == 1 + level
+        ]
+        if len(track_indices_l) == 0:  # Nothing to match at this level
+            continue
+
+        matches_l, _, unmatched_detections = \
+            min_cost_matching(
+                distance_metric, max_distance, tracks, detections,
+                track_indices_l, unmatched_detections)
+        matches += matches_l
+    unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))
+    return matches, unmatched_tracks, unmatched_detections
+
+
+def gate_cost_matrix(
+        kf, cost_matrix, tracks, detections, track_indices, detection_indices,
+        gated_cost=INFTY_COST, only_position=False):
+    """Invalidate infeasible entries in cost matrix based on the state
+    distributions obtained by Kalman filtering.
+
+    Parameters
+    ----------
+    kf : The Kalman filter.
+    cost_matrix : ndarray
+        The NxM dimensional cost matrix, where N is the number of track indices
+        and M is the number of detection indices, such that entry (i, j) is the
+        association cost between `tracks[track_indices[i]]` and
+        `detections[detection_indices[j]]`.
+    tracks : List[track.Track]
+        A list of predicted tracks at the current time step.
+    detections : List[detection.Detection]
+        A list of detections at the current time step.
+    track_indices : List[int]
+        List of track indices that maps rows in `cost_matrix` to tracks in
+        `tracks` (see description above).
+    detection_indices : List[int]
+        List of detection indices that maps columns in `cost_matrix` to
+        detections in `detections` (see description above).
+    gated_cost : Optional[float]
+        Entries in the cost matrix corresponding to infeasible associations are
+        set this value. Defaults to a very large value.
+    only_position : Optional[bool]
+        If True, only the x, y position of the state distribution is considered
+        during gating. Defaults to False.
+
+    Returns
+    -------
+    ndarray
+        Returns the modified cost matrix.
+
+    """
+    gating_dim = 2 if only_position else 4
+    gating_threshold = kalman_filter.chi2inv95[gating_dim]
+    measurements = np.asarray(
+        [detections[i].to_xyah() for i in detection_indices])
+    for row, track_idx in enumerate(track_indices):
+        track = tracks[track_idx]
+        gating_distance = kf.gating_distance(
+            track.mean, track.covariance, measurements, only_position)
+        cost_matrix[row, gating_distance > gating_threshold] = gated_cost
+    return cost_matrix

deep_sort/sort/nn_matching.py

@@ -0,0 +1,176 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+
+
+def _pdist(a, b):
+    """Compute pair-wise squared distance between points in `a` and `b`.
+
+    Parameters
+    ----------
+    a : array_like
+        An NxM matrix of N samples of dimensionality M.
+    b : array_like
+        An LxM matrix of L samples of dimensionality M.
+
+    Returns
+    -------
+    ndarray
+        Returns a matrix of size len(a), len(b) such that eleement (i, j)
+        contains the squared distance between `a[i]` and `b[j]`.
+
+    """
+    a, b = np.asarray(a), np.asarray(b)
+    if len(a) == 0 or len(b) == 0:
+        return np.zeros((len(a), len(b)))
+    a2, b2 = np.square(a).sum(axis=1), np.square(b).sum(axis=1)
+    r2 = -2. * np.dot(a, b.T) + a2[:, None] + b2[None, :]
+    r2 = np.clip(r2, 0., float(np.inf))
+    return r2
+
+
+def _cosine_distance(a, b, data_is_normalized=False):
+    """Compute pair-wise cosine distance between points in `a` and `b`.
+
+    Parameters
+    ----------
+    a : array_like
+        An NxM matrix of N samples of dimensionality M.
+    b : array_like
+        An LxM matrix of L samples of dimensionality M.
+    data_is_normalized : Optional[bool]
+        If True, assumes rows in a and b are unit length vectors.
+        Otherwise, a and b are explicitly normalized to lenght 1.
+
+    Returns
+    -------
+    ndarray
+        Returns a matrix of size len(a), len(b) such that eleement (i, j)
+        contains the squared distance between `a[i]` and `b[j]`.
+
+    """
+    if not data_is_normalized:
+        a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
+        b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
+    return 1. - np.dot(a, b.T)
+
+
+def _nn_euclidean_distance(x, y):
+    """ Helper function for nearest neighbor distance metric (Euclidean).
+
+    Parameters
+    ----------
+    x : ndarray
+        A matrix of N row-vectors (sample points).
+    y : ndarray
+        A matrix of M row-vectors (query points).
+
+    Returns
+    -------
+    ndarray
+        A vector of length M that contains for each entry in `y` the
+        smallest Euclidean distance to a sample in `x`.
+
+    """
+    distances = _pdist(x, y)
+    return np.maximum(0.0, distances.min(axis=0))
+
+
+def _nn_cosine_distance(x, y):
+    """ Helper function for nearest neighbor distance metric (cosine).
+
+    Parameters
+    ----------
+    x : ndarray
+        A matrix of N row-vectors (sample points).
+    y : ndarray
+        A matrix of M row-vectors (query points).
+
+    Returns
+    -------
+    ndarray
+        A vector of length M that contains for each entry in `y` the
+        smallest cosine distance to a sample in `x`.
+
+    """
+    distances = _cosine_distance(x, y)
+    return distances.min(axis=0)
+
+
+class NearestNeighborDistanceMetric(object):
+    """
+    A nearest neighbor distance metric that, for each target, returns
+    the closest distance to any sample that has been observed so far.
+
+    Parameters
+    ----------
+    metric : str
+        Either "euclidean" or "cosine".
+    matching_threshold: float
+        The matching threshold. Samples with larger distance are considered an
+        invalid match.
+    budget : Optional[int]
+        If not None, fix samples per class to at most this number. Removes
+        the oldest samples when the budget is reached.
+
+    Attributes
+    ----------
+    samples : Dict[int -> List[ndarray]]
+        A dictionary that maps from target identities to the list of samples
+        that have been observed so far.
+
+    """
+
+    def __init__(self, metric, matching_threshold, budget=None):
+
+        if metric == "euclidean":
+            self._metric = _nn_euclidean_distance
+        elif metric == "cosine":
+            self._metric = _nn_cosine_distance
+        else:
+            raise ValueError(
+                "Invalid metric; must be either 'euclidean' or 'cosine'")
+        self.matching_threshold = matching_threshold
+        self.budget = budget
+        self.samples = {}
+
+    def partial_fit(self, features, targets, active_targets):
+        """Update the distance metric with new data.
+
+        Parameters
+        ----------
+        features : ndarray
+            An NxM matrix of N features of dimensionality M.
+        targets : ndarray
+            An integer array of associated target identities.
+        active_targets : List[int]
+            A list of targets that are currently present in the scene.
+
+        """
+        for feature, target in zip(features, targets):
+            self.samples.setdefault(target, []).append(feature)
+            if self.budget is not None:
+                self.samples[target] = self.samples[target][-self.budget:]
+        self.samples = {k: self.samples[k] for k in active_targets}
+
+    def distance(self, features, targets):
+        """Compute distance between features and targets.
+
+        Parameters
+        ----------
+        features : ndarray
+            An NxM matrix of N features of dimensionality M.
+        targets : List[int]
+            A list of targets to match the given `features` against.
+
+        Returns
+        -------
+        ndarray
+            Returns a cost matrix of shape len(targets), len(features), where
+            element (i, j) contains the closest squared distance between
+            `targets[i]` and `features[j]`.
+
+        """
+        cost_matrix = np.zeros((len(targets), len(features)))
+        for i, target in enumerate(targets):
+            cost_matrix[i, :] = self._metric(self.samples[target], features)
+        return cost_matrix

deep_sort/sort/preprocessing.py

@@ -0,0 +1,73 @@
+# vim: expandtab:ts=4:sw=4
+import numpy as np
+import cv2
+
+
+def non_max_suppression(boxes, max_bbox_overlap, scores=None):
+    """Suppress overlapping detections.
+
+    Original code from [1]_ has been adapted to include confidence score.
+
+    .. [1] http://www.pyimagesearch.com/2015/02/16/
+           faster-non-maximum-suppression-python/
+
+    Examples
+    --------
+
+        >>> boxes = [d.roi for d in detections]
+        >>> scores = [d.confidence for d in detections]
+        >>> indices = non_max_suppression(boxes, max_bbox_overlap, scores)
+        >>> detections = [detections[i] for i in indices]
+
+    Parameters
+    ----------
+    boxes : ndarray
+        Array of ROIs (x, y, width, height).
+    max_bbox_overlap : float
+        ROIs that overlap more than this values are suppressed.
+    scores : Optional[array_like]
+        Detector confidence score.
+
+    Returns
+    -------
+    List[int]
+        Returns indices of detections that have survived non-maxima suppression.
+
+    """
+    if len(boxes) == 0:
+        return []
+
+    boxes = boxes.astype(np.float32)
+    pick = []
+
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2] + boxes[:, 0]
+    y2 = boxes[:, 3] + boxes[:, 1]
+
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    if scores is not None:
+        idxs = np.argsort(scores)
+    else:
+        idxs = np.argsort(y2)
+
+    while len(idxs) > 0:
+        last = len(idxs) - 1
+        i = idxs[last]
+        pick.append(i)
+
+        xx1 = np.maximum(x1[i], x1[idxs[:last]])
+        yy1 = np.maximum(y1[i], y1[idxs[:last]])
+        xx2 = np.minimum(x2[i], x2[idxs[:last]])
+        yy2 = np.minimum(y2[i], y2[idxs[:last]])
+
+        w = np.maximum(0, xx2 - xx1 + 1)
+        h = np.maximum(0, yy2 - yy1 + 1)
+
+        overlap = (w * h) / (area[idxs[:last]] + area[idxs[last]] - w * h)
+
+        idxs = np.delete(
+            idxs, np.concatenate(
+                ([last], np.where(overlap > max_bbox_overlap)[0])))
+
+    return pick

deep_sort/sort/track.py

@@ -0,0 +1,169 @@
+# vim: expandtab:ts=4:sw=4
+
+
+class TrackState:
+    """
+    Enumeration type for the single target track state. Newly created tracks are
+    classified as `tentative` until enough evidence has been collected. Then,
+    the track state is changed to `confirmed`. Tracks that are no longer alive
+    are classified as `deleted` to mark them for removal from the set of active
+    tracks.
+
+    """
+
+    Tentative = 1
+    Confirmed = 2
+    Deleted = 3
+
+
+class Track:
+    """
+    A single target track with state space `(x, y, a, h)` and associated
+    velocities, where `(x, y)` is the center of the bounding box, `a` is the
+    aspect ratio and `h` is the height.
+
+    Parameters
+    ----------
+    mean : ndarray
+        Mean vector of the initial state distribution.
+    covariance : ndarray
+        Covariance matrix of the initial state distribution.
+    track_id : int
+        A unique track identifier.
+    n_init : int
+        Number of consecutive detections before the track is confirmed. The
+        track state is set to `Deleted` if a miss occurs within the first
+        `n_init` frames.
+    max_age : int
+        The maximum number of consecutive misses before the track state is
+        set to `Deleted`.
+    feature : Optional[ndarray]
+        Feature vector of the detection this track originates from. If not None,
+        this feature is added to the `features` cache.
+
+    Attributes
+    ----------
+    mean : ndarray
+        Mean vector of the initial state distribution.
+    covariance : ndarray
+        Covariance matrix of the initial state distribution.
+    track_id : int
+        A unique track identifier.
+    hits : int
+        Total number of measurement updates.
+    age : int
+        Total number of frames since first occurance.
+    time_since_update : int
+        Total number of frames since last measurement update.
+    state : TrackState
+        The current track state.
+    features : List[ndarray]
+        A cache of features. On each measurement update, the associated feature
+        vector is added to this list.
+
+    """
+
+    def __init__(self, mean, covariance, track_id, n_init, max_age,
+                 feature=None, cls=None, mask=None):
+        self.mean = mean
+        self.covariance = covariance
+        self.track_id = track_id
+        self.hits = 1
+        self.age = 1
+        self.time_since_update = 0
+
+        self.state = TrackState.Tentative
+        self.cls = cls
+        self.mask = mask
+        self.features = []
+        if feature is not None:
+            self.features.append(feature)
+
+        self._n_init = n_init
+        self._max_age = max_age
+
+    def to_tlwh(self):
+        """Get current position in bounding box format `(top left x, top left y,
+        width, height)`.
+
+        Returns
+        -------
+        ndarray
+            The bounding box.
+
+        """
+        ret = self.mean[:4].copy()
+        ret[2] *= ret[3]
+        ret[:2] -= ret[2:] / 2
+        return ret
+
+    def to_tlbr(self):
+        """Get current position in bounding box format `(min x, miny, max x,
+        max y)`.
+
+        Returns
+        -------
+        ndarray
+            The bounding box.
+
+        """
+        ret = self.to_tlwh()
+        ret[2:] = ret[:2] + ret[2:]
+        return ret
+
+    def predict(self, kf):
+        """Propagate the state distribution to the current time step using a
+        Kalman filter prediction step.
+
+        Parameters
+        ----------
+        kf : kalman_filter.KalmanFilter
+            The Kalman filter.
+
+        """
+        self.mean, self.covariance = kf.predict(self.mean, self.covariance)
+        self.age += 1
+        self.time_since_update += 1
+
+    def update(self, kf, detection):
+        """Perform Kalman filter measurement update step and update the feature
+        cache.
+
+        Parameters
+        ----------
+        kf : kalman_filter.KalmanFilter
+            The Kalman filter.
+        detection : Detection
+            The associated detection.
+
+        """
+        self.mask = detection.mask
+        self.mean, self.covariance = kf.update(
+            self.mean, self.covariance, detection.to_xyah())
+        self.features.append(detection.feature)
+
+        self.hits += 1
+        self.time_since_update = 0
+        if self.state == TrackState.Tentative and self.hits >= self._n_init:
+            self.state = TrackState.Confirmed
+
+    def mark_missed(self):
+        """Mark this track as missed (no association at the current time step).
+        """
+        if self.state == TrackState.Tentative:
+            self.state = TrackState.Deleted
+        elif self.time_since_update > self._max_age:
+            self.state = TrackState.Deleted
+
+    def is_tentative(self):
+        """Returns True if this track is tentative (unconfirmed).
+        """
+        return self.state == TrackState.Tentative
+
+    def is_confirmed(self):
+        """Returns True if this track is confirmed."""
+        return self.state == TrackState.Confirmed
+
+    def is_deleted(self):
+        """Returns True if this track is dead and should be deleted."""
+        return self.state == TrackState.Deleted

deep_sort/sort/tracker.py

@@ -0,0 +1,138 @@
+# vim: expandtab:ts=4:sw=4
+from __future__ import absolute_import
+import numpy as np
+from . import kalman_filter
+from . import linear_assignment
+from . import iou_matching
+from .track import Track
+
+
+class Tracker:
+    """
+    This is the multi-target tracker.
+
+    Parameters
+    ----------
+    metric : nn_matching.NearestNeighborDistanceMetric
+        A distance metric for measurement-to-track association.
+    max_age : int
+        Maximum number of missed misses before a track is deleted.
+    n_init : int
+        Number of consecutive detections before the track is confirmed. The
+        track state is set to `Deleted` if a miss occurs within the first
+        `n_init` frames.
+
+    Attributes
+    ----------
+    metric : nn_matching.NearestNeighborDistanceMetric
+        The distance metric used for measurement to track association.
+    max_age : int
+        Maximum number of missed misses before a track is deleted.
+    n_init : int
+        Number of frames that a track remains in initialization phase.
+    kf : kalman_filter.KalmanFilter
+        A Kalman filter to filter target trajectories in image space.
+    tracks : List[Track]
+        The list of active tracks at the current time step.
+
+    """
+
+    def __init__(self, metric, max_iou_distance=0.7, max_age=70, n_init=3):
+        self.metric = metric
+        self.max_iou_distance = max_iou_distance
+        self.max_age = max_age
+        self.n_init = n_init
+
+        self.kf = kalman_filter.KalmanFilter()
+        self.tracks = []
+        self._next_id = 1
+
+    def predict(self):
+        """Propagate track state distributions one time step forward.
+
+        This function should be called once every time step, before `update`.
+        """
+        for track in self.tracks:
+            track.predict(self.kf)
+
+    def update(self, detections):
+        """Perform measurement update and track management.
+
+        Parameters
+        ----------
+        detections : List[deep_sort.detection.Detection]
+            A list of detections at the current time step.
+
+        """
+        # Run matching cascade.
+        matches, unmatched_tracks, unmatched_detections = \
+            self._match(detections)
+
+        # Update track set.
+        for track_idx, detection_idx in matches:
+            self.tracks[track_idx].update(
+                self.kf, detections[detection_idx])
+        for track_idx in unmatched_tracks:
+            self.tracks[track_idx].mark_missed()
+        for detection_idx in unmatched_detections:
+            self._initiate_track(detections[detection_idx])
+        self.tracks = [t for t in self.tracks if not t.is_deleted()]
+
+        # Update distance metric.
+        active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
+        features, targets = [], []
+        for track in self.tracks:
+            if not track.is_confirmed():
+                continue
+            features += track.features
+            targets += [track.track_id for _ in track.features]
+            track.features = []
+        self.metric.partial_fit(
+            np.asarray(features), np.asarray(targets), active_targets)
+
+    def _match(self, detections):
+
+        def gated_metric(tracks, dets, track_indices, detection_indices):
+            features = np.array([dets[i].feature for i in detection_indices])
+            targets = np.array([tracks[i].track_id for i in track_indices])
+            cost_matrix = self.metric.distance(features, targets)
+            cost_matrix = linear_assignment.gate_cost_matrix(
+                self.kf, cost_matrix, tracks, dets, track_indices,
+                detection_indices)
+
+            return cost_matrix
+
+        # Split track set into confirmed and unconfirmed tracks.
+        confirmed_tracks = [
+            i for i, t in enumerate(self.tracks) if t.is_confirmed()]
+        unconfirmed_tracks = [
+            i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
+
+        # Associate confirmed tracks using appearance features.
+        matches_a, unmatched_tracks_a, unmatched_detections = \
+            linear_assignment.matching_cascade(
+                gated_metric, self.metric.matching_threshold, self.max_age,
+                self.tracks, detections, confirmed_tracks)
+
+        # Associate remaining tracks together with unconfirmed tracks using IOU.
+        iou_track_candidates = unconfirmed_tracks + [
+            k for k in unmatched_tracks_a if
+            self.tracks[k].time_since_update == 1]
+        unmatched_tracks_a = [
+            k for k in unmatched_tracks_a if
+            self.tracks[k].time_since_update != 1]
+        matches_b, unmatched_tracks_b, unmatched_detections = \
+            linear_assignment.min_cost_matching(
+                iou_matching.iou_cost, self.max_iou_distance, self.tracks,
+                detections, iou_track_candidates, unmatched_detections)
+
+        matches = matches_a + matches_b
+        unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
+        return matches, unmatched_tracks, unmatched_detections
+
+    def _initiate_track(self, detection):
+        mean, covariance = self.kf.initiate(detection.to_xyah())
+        self.tracks.append(Track(
+            mean, covariance, self._next_id, self.n_init, self.max_age,
+            detection.feature, detection.cls, detection.mask))
+        self._next_id += 1

deep_sort/utils/__init__.py

@@ -0,0 +1,2 @@
+def datasets():
+    return None

deep_sort/utils/asserts.py

@@ -0,0 +1,13 @@
+from os import environ
+
+
+def assert_in(file, files_to_check):
+    if file not in files_to_check:
+        raise AssertionError("{} does not exist in the list".format(str(file)))
+    return True
+
+
+def assert_in_env(check_list: list):
+    for item in check_list:
+        assert_in(item, environ.keys())
+    return True

deep_sort/utils/draw.py

@@ -0,0 +1,51 @@
+import numpy as np
+import cv2
+
+palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
+
+
+def compute_color_for_labels(label):
+    """
+    Simple function that adds fixed color depending on the class
+    """
+    color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
+    return tuple(color)
+
+
+def draw_masks(image, mask, color, thresh: float = 0.7, alpha: float = 0.5):
+    np_image = np.asarray(image)
+    mask = mask > thresh
+
+    color = np.asarray(color)
+    img_to_draw = np.copy(np_image)
+    # TODO: There might be a way to vectorize this
+    img_to_draw[mask] = color
+
+    out = np_image * (1 - alpha) + img_to_draw * alpha
+    return out.astype(np.uint8)
+
+
+def draw_boxes(img, bbox, names=None, identities=None, masks=None, offset=(0, 0)):
+    for i, box in enumerate(bbox):
+        x1, y1, x2, y2 = [int(i) for i in box]
+        x1 += offset[0]
+        x2 += offset[0]
+        y1 += offset[1]
+        y2 += offset[1]
+        # box text and bar
+        id = int(identities[i]) if identities is not None else 0
+        color = compute_color_for_labels(id)
+        label = '{:}{:d}'.format(names[i], id)
+        t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
+        if masks is not None:
+            mask = masks[i]
+            img = draw_masks(img, mask, color)
+        cv2.rectangle(img, (x1, y1), (x2, y2), color, 3)
+        cv2.rectangle(img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1)
+        cv2.putText(img, label, (x1, y1 + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2)
+    return img
+
+
+if __name__ == '__main__':
+    for i in range(82):
+        print(compute_color_for_labels(i))

deep_sort/utils/evaluation.py

@@ -0,0 +1,103 @@
+import os
+import numpy as np
+import copy
+import motmetrics as mm
+mm.lap.default_solver = 'lap'
+from utils.io import read_results, unzip_objs
+
+
+class Evaluator(object):
+
+    def __init__(self, data_root, seq_name, data_type):
+        self.data_root = data_root
+        self.seq_name = seq_name
+        self.data_type = data_type
+
+        self.load_annotations()
+        self.reset_accumulator()
+
+    def load_annotations(self):
+        assert self.data_type == 'mot'
+
+        gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')
+        self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)
+        self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)
+
+    def reset_accumulator(self):
+        self.acc = mm.MOTAccumulator(auto_id=True)
+
+    def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
+        # results
+        trk_tlwhs = np.copy(trk_tlwhs)
+        trk_ids = np.copy(trk_ids)
+
+        # gts
+        gt_objs = self.gt_frame_dict.get(frame_id, [])
+        gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
+
+        # ignore boxes
+        ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
+        ignore_tlwhs = unzip_objs(ignore_objs)[0]
+
+
+        # remove ignored results
+        keep = np.ones(len(trk_tlwhs), dtype=bool)
+        iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)
+        if len(iou_distance) > 0:
+            match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
+            match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
+            match_ious = iou_distance[match_is, match_js]
+
+            match_js = np.asarray(match_js, dtype=int)
+            match_js = match_js[np.logical_not(np.isnan(match_ious))]
+            keep[match_js] = False
+            trk_tlwhs = trk_tlwhs[keep]
+            trk_ids = trk_ids[keep]
+
+        # get distance matrix
+        iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
+
+        # acc
+        self.acc.update(gt_ids, trk_ids, iou_distance)
+
+        if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):
+            events = self.acc.last_mot_events  # only supported by https://github.com/longcw/py-motmetrics
+        else:
+            events = None
+        return events
+
+    def eval_file(self, filename):
+        self.reset_accumulator()
+
+        result_frame_dict = read_results(filename, self.data_type, is_gt=False)
+        frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))
+        for frame_id in frames:
+            trk_objs = result_frame_dict.get(frame_id, [])
+            trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
+            self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
+
+        return self.acc
+
+    @staticmethod
+    def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):
+        names = copy.deepcopy(names)
+        if metrics is None:
+            metrics = mm.metrics.motchallenge_metrics
+        metrics = copy.deepcopy(metrics)
+
+        mh = mm.metrics.create()
+        summary = mh.compute_many(
+            accs,
+            metrics=metrics,
+            names=names,
+            generate_overall=True
+        )
+
+        return summary
+
+    @staticmethod
+    def save_summary(summary, filename):
+        import pandas as pd
+        writer = pd.ExcelWriter(filename)
+        summary.to_excel(writer)
+        writer.save()

deep_sort/utils/io.py

@@ -0,0 +1,133 @@
+import os
+from typing import Dict
+import numpy as np
+
+# from utils.log import get_logger
+
+
+def write_results(filename, results, data_type):
+    if data_type == 'mot':
+        save_format = '{frame},{id},{cls},{x1},{y1},{w},{h},-1,-1,-1,-1\n'
+    elif data_type == 'kitti':
+        save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
+    else:
+        raise ValueError(data_type)
+
+    with open(filename, 'w') as f:
+        for frame_id, tlwhs, track_ids, classes in results:
+            if data_type == 'kitti':
+                frame_id -= 1
+            for tlwh, track_id, cls_id in zip(tlwhs, track_ids, classes):
+                if track_id < 0:
+                    continue
+                x1, y1, w, h = tlwh
+                x2, y2 = x1 + w, y1 + h
+                line = save_format.format(frame=frame_id, id=track_id, cls=cls_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h)
+                f.write(line)
+
+
+# def write_results(filename, results_dict: Dict, data_type: str):
+#     if not filename:
+#         return
+#     path = os.path.dirname(filename)
+#     if not os.path.exists(path):
+#         os.makedirs(path)
+
+#     if data_type in ('mot', 'mcmot', 'lab'):
+#         save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
+#     elif data_type == 'kitti':
+#         save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n'
+#     else:
+#         raise ValueError(data_type)
+
+#     with open(filename, 'w') as f:
+#         for frame_id, frame_data in results_dict.items():
+#             if data_type == 'kitti':
+#                 frame_id -= 1
+#             for tlwh, track_id in frame_data:
+#                 if track_id < 0:
+#                     continue
+#                 x1, y1, w, h = tlwh
+#                 x2, y2 = x1 + w, y1 + h
+#                 line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0)
+#                 f.write(line)
+#     logger.info('Save results to {}'.format(filename))
+
+
+def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
+    if data_type in ('mot', 'lab'):
+        read_fun = read_mot_results
+    else:
+        raise ValueError('Unknown data type: {}'.format(data_type))
+
+    return read_fun(filename, is_gt, is_ignore)
+
+
+"""
+labels={'ped', ...			% 1
+'person_on_vhcl', ...	% 2
+'car', ...				% 3
+'bicycle', ...			% 4
+'mbike', ...			% 5
+'non_mot_vhcl', ...		% 6
+'static_person', ...	% 7
+'distractor', ...		% 8
+'occluder', ...			% 9
+'occluder_on_grnd', ...		%10
+'occluder_full', ...		% 11
+'reflection', ...		% 12
+'crowd' ...			% 13
+};
+"""
+
+
+def read_mot_results(filename, is_gt, is_ignore):
+    valid_labels = {1}
+    ignore_labels = {2, 7, 8, 12}
+    results_dict = dict()
+    if os.path.isfile(filename):
+        with open(filename, 'r') as f:
+            for line in f.readlines():
+                linelist = line.split(',')
+                if len(linelist) < 7:
+                    continue
+                fid = int(linelist[0])
+                if fid < 1:
+                    continue
+                results_dict.setdefault(fid, list())
+
+                if is_gt:
+                    if 'MOT16-' in filename or 'MOT17-' in filename:
+                        label = int(float(linelist[7]))
+                        mark = int(float(linelist[6]))
+                        if mark == 0 or label not in valid_labels:
+                            continue
+                    score = 1
+                elif is_ignore:
+                    if 'MOT16-' in filename or 'MOT17-' in filename:
+                        label = int(float(linelist[7]))
+                        vis_ratio = float(linelist[8])
+                        if label not in ignore_labels and vis_ratio >= 0:
+                            continue
+                    else:
+                        continue
+                    score = 1
+                else:
+                    score = float(linelist[6])
+
+                tlwh = tuple(map(float, linelist[2:6]))
+                target_id = int(linelist[1])
+
+                results_dict[fid].append((tlwh, target_id, score))
+
+    return results_dict
+
+
+def unzip_objs(objs):
+    if len(objs) > 0:
+        tlwhs, ids, scores = zip(*objs)
+    else:
+        tlwhs, ids, scores = [], [], []
+    tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
+
+    return tlwhs, ids, scores

deep_sort/utils/json_logger.py

@@ -0,0 +1,383 @@
+"""
+References:
+    https://medium.com/analytics-vidhya/creating-a-custom-logging-mechanism-for-real-time-object-detection-using-tdd-4ca2cfcd0a2f
+"""
+import json
+from os import makedirs
+from os.path import exists, join
+from datetime import datetime
+
+
+class JsonMeta(object):
+    HOURS = 3
+    MINUTES = 59
+    SECONDS = 59
+    PATH_TO_SAVE = 'LOGS'
+    DEFAULT_FILE_NAME = 'remaining'
+
+
+class BaseJsonLogger(object):
+    """
+    This is the base class that returns __dict__ of its own
+    it also returns the dicts of objects in the attributes that are list instances
+
+    """
+
+    def dic(self):
+        # returns dicts of objects
+        out = {}
+        for k, v in self.__dict__.items():
+            if hasattr(v, 'dic'):
+                out[k] = v.dic()
+            elif isinstance(v, list):
+                out[k] = self.list(v)
+            else:
+                out[k] = v
+        return out
+
+    @staticmethod
+    def list(values):
+        # applies the dic method on items in the list
+        return [v.dic() if hasattr(v, 'dic') else v for v in values]
+
+
+class Label(BaseJsonLogger):
+    """
+    For each bounding box there are various categories with confidences. Label class keeps track of that information.
+    """
+
+    def __init__(self, category: str, confidence: float):
+        self.category = category
+        self.confidence = confidence
+
+
+class Bbox(BaseJsonLogger):
+    """
+    This module stores the information for each frame and use them in JsonParser
+    Attributes:
+        labels (list): List of label module.
+        top (int):
+        left (int):
+        width (int):
+        height (int):
+
+    Args:
+        bbox_id (float):
+        top (int):
+        left (int):
+        width (int):
+        height (int):
+
+    References:
+        Check Label module for better understanding.
+
+
+    """
+
+    def __init__(self, bbox_id, top, left, width, height):
+        self.labels = []
+        self.bbox_id = bbox_id
+        self.top = top
+        self.left = left
+        self.width = width
+        self.height = height
+
+    def add_label(self, category, confidence):
+        # adds category and confidence only if top_k is not exceeded.
+        self.labels.append(Label(category, confidence))
+
+    def labels_full(self, value):
+        return len(self.labels) == value
+
+
+class Frame(BaseJsonLogger):
+    """
+    This module stores the information for each frame and use them in JsonParser
+    Attributes:
+        timestamp (float): The elapsed time of captured frame
+        frame_id (int): The frame number of the captured video
+        bboxes (list of Bbox objects): Stores the list of bbox objects.
+
+    References:
+        Check Bbox class for better information
+
+    Args:
+        timestamp (float):
+        frame_id (int):
+
+    """
+
+    def __init__(self, frame_id: int, timestamp: float = None):
+        self.frame_id = frame_id
+        self.timestamp = timestamp
+        self.bboxes = []
+
+    def add_bbox(self, bbox_id: int, top: int, left: int, width: int, height: int):
+        bboxes_ids = [bbox.bbox_id for bbox in self.bboxes]
+        if bbox_id not in bboxes_ids:
+            self.bboxes.append(Bbox(bbox_id, top, left, width, height))
+        else:
+            raise ValueError("Frame with id: {} already has a Bbox with id: {}".format(self.frame_id, bbox_id))
+
+    def add_label_to_bbox(self, bbox_id: int, category: str, confidence: float):
+        bboxes = {bbox.id: bbox for bbox in self.bboxes}
+        if bbox_id in bboxes.keys():
+            res = bboxes.get(bbox_id)
+            res.add_label(category, confidence)
+        else:
+            raise ValueError('the bbox with id: {} does not exists!'.format(bbox_id))
+
+
+class BboxToJsonLogger(BaseJsonLogger):
+    """
+    ُ This module is designed to automate the task of logging jsons. An example json is used
+    to show the contents of json file shortly
+    Example:
+          {
+          "video_details": {
+            "frame_width": 1920,
+            "frame_height": 1080,
+            "frame_rate": 20,
+            "video_name": "/home/gpu/codes/MSD/pedestrian_2/project/public/camera1.avi"
+          },
+          "frames": [
+            {
+              "frame_id": 329,
+              "timestamp": 3365.1254
+              "bboxes": [
+                {
+                  "labels": [
+                    {
+                      "category": "pedestrian",
+                      "confidence": 0.9
+                    }
+                  ],
+                  "bbox_id": 0,
+                  "top": 1257,
+                  "left": 138,
+                  "width": 68,
+                  "height": 109
+                }
+              ]
+            }],
+
+    Attributes:
+        frames (dict): It's a dictionary that maps each frame_id to json attributes.
+        video_details (dict): information about video file.
+        top_k_labels (int): shows the allowed number of labels
+        start_time (datetime object): we use it to automate the json output by time.
+
+    Args:
+        top_k_labels (int): shows the allowed number of labels
+
+    """
+
+    def __init__(self, top_k_labels: int = 1):
+        self.frames = {}
+        self.video_details = self.video_details = dict(frame_width=None, frame_height=None, frame_rate=None,
+                                                       video_name=None)
+        self.top_k_labels = top_k_labels
+        self.start_time = datetime.now()
+
+    def set_top_k(self, value):
+        self.top_k_labels = value
+
+    def frame_exists(self, frame_id: int) -> bool:
+        """
+        Args:
+            frame_id (int):
+
+        Returns:
+            bool: true if frame_id is recognized
+        """
+        return frame_id in self.frames.keys()
+
+    def add_frame(self, frame_id: int, timestamp: float = None) -> None:
+        """
+        Args:
+            frame_id (int):
+            timestamp (float): opencv captured frame time property
+
+        Raises:
+             ValueError: if frame_id would not exist in class frames attribute
+
+        Returns:
+            None
+
+        """
+        if not self.frame_exists(frame_id):
+            self.frames[frame_id] = Frame(frame_id, timestamp)
+        else:
+            raise ValueError("Frame id: {} already exists".format(frame_id))
+
+    def bbox_exists(self, frame_id: int, bbox_id: int) -> bool:
+        """
+        Args:
+            frame_id:
+            bbox_id:
+
+        Returns:
+            bool: if bbox exists in frame bboxes list
+        """
+        bboxes = []
+        if self.frame_exists(frame_id=frame_id):
+            bboxes = [bbox.bbox_id for bbox in self.frames[frame_id].bboxes]
+        return bbox_id in bboxes
+
+    def find_bbox(self, frame_id: int, bbox_id: int):
+        """
+
+        Args:
+            frame_id:
+            bbox_id:
+
+        Returns:
+            bbox_id (int):
+
+        Raises:
+            ValueError: if bbox_id does not exist in the bbox list of specific frame.
+        """
+        if not self.bbox_exists(frame_id, bbox_id):
+            raise ValueError("frame with id: {} does not contain bbox with id: {}".format(frame_id, bbox_id))
+        bboxes = {bbox.bbox_id: bbox for bbox in self.frames[frame_id].bboxes}
+        return bboxes.get(bbox_id)
+
+    def add_bbox_to_frame(self, frame_id: int, bbox_id: int, top: int, left: int, width: int, height: int) -> None:
+        """
+
+        Args:
+            frame_id (int):
+            bbox_id (int):
+            top (int):
+            left (int):
+            width (int):
+            height (int):
+
+        Returns:
+            None
+
+        Raises:
+            ValueError: if bbox_id already exist in frame information with frame_id
+            ValueError: if frame_id does not exist in frames attribute
+        """
+        if self.frame_exists(frame_id):
+            frame = self.frames[frame_id]
+            if not self.bbox_exists(frame_id, bbox_id):
+                frame.add_bbox(bbox_id, top, left, width, height)
+            else:
+                raise ValueError(
+                    "frame with frame_id: {} already contains the bbox with id: {} ".format(frame_id, bbox_id))
+        else:
+            raise ValueError("frame with frame_id: {} does not exist".format(frame_id))
+
+    def add_label_to_bbox(self, frame_id: int, bbox_id: int, category: str, confidence: float):
+        """
+        Args:
+            frame_id:
+            bbox_id:
+            category:
+            confidence: the confidence value returned from yolo detection
+
+        Returns:
+            None
+
+        Raises:
+            ValueError: if labels quota (top_k_labels) exceeds.
+        """
+        bbox = self.find_bbox(frame_id, bbox_id)
+        if not bbox.labels_full(self.top_k_labels):
+            bbox.add_label(category, confidence)
+        else:
+            raise ValueError("labels in frame_id: {}, bbox_id: {} is fulled".format(frame_id, bbox_id))
+
+    def add_video_details(self, frame_width: int = None, frame_height: int = None, frame_rate: int = None,
+                          video_name: str = None):
+        self.video_details['frame_width'] = frame_width
+        self.video_details['frame_height'] = frame_height
+        self.video_details['frame_rate'] = frame_rate
+        self.video_details['video_name'] = video_name
+
+    def output(self):
+        output = {'video_details': self.video_details}
+        result = list(self.frames.values())
+        output['frames'] = [item.dic() for item in result]
+        return output
+
+    def json_output(self, output_name):
+        """
+        Args:
+            output_name:
+
+        Returns:
+            None
+
+        Notes:
+            It creates the json output with `output_name` name.
+        """
+        if not output_name.endswith('.json'):
+            output_name += '.json'
+        with open(output_name, 'w') as file:
+            json.dump(self.output(), file)
+        file.close()
+
+    def set_start(self):
+        self.start_time = datetime.now()
+
+    def schedule_output_by_time(self, output_dir=JsonMeta.PATH_TO_SAVE, hours: int = 0, minutes: int = 0,
+                                seconds: int = 60) -> None:
+        """
+        Notes:
+            Creates folder and then periodically stores the jsons on that address.
+
+        Args:
+            output_dir (str): the directory where output files will be stored
+            hours (int):
+            minutes (int):
+            seconds (int):
+
+        Returns:
+            None
+
+        """
+        end = datetime.now()
+        interval = 0
+        interval += abs(min([hours, JsonMeta.HOURS]) * 3600)
+        interval += abs(min([minutes, JsonMeta.MINUTES]) * 60)
+        interval += abs(min([seconds, JsonMeta.SECONDS]))
+        diff = (end - self.start_time).seconds
+
+        if diff > interval:
+            output_name = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '.json'
+            if not exists(output_dir):
+                makedirs(output_dir)
+            output = join(output_dir, output_name)
+            self.json_output(output_name=output)
+            self.frames = {}
+            self.start_time = datetime.now()
+
+    def schedule_output_by_frames(self, frames_quota, frame_counter, output_dir=JsonMeta.PATH_TO_SAVE):
+        """
+        saves as the number of frames quota increases higher.
+        :param frames_quota:
+        :param frame_counter:
+        :param output_dir:
+        :return:
+        """
+        pass
+
+    def flush(self, output_dir):
+        """
+        Notes:
+            We use this function to output jsons whenever possible.
+            like the time that we exit the while loop of opencv.
+
+        Args:
+            output_dir:
+
+        Returns:
+            None
+
+        """
+        filename = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '-remaining.json'
+        output = join(output_dir, filename)
+        self.json_output(output_name=output)

deep_sort/utils/log.py

@@ -0,0 +1,17 @@
+import logging
+
+
+def get_logger(name='root'):
+    formatter = logging.Formatter(
+        # fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s')
+        fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+
+    handler = logging.StreamHandler()
+    handler.setFormatter(formatter)
+
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.INFO)
+    logger.addHandler(handler)
+    return logger
+
+

deep_sort/utils/parser.py

@@ -0,0 +1,38 @@
+import os
+import yaml
+from easydict import EasyDict as edict
+
+class YamlParser(edict):
+    """
+    This is yaml parser based on EasyDict.
+    """
+    def __init__(self, cfg_dict=None, config_file=None):
+        if cfg_dict is None:
+            cfg_dict = {}
+
+        if config_file is not None:
+            assert (os.path.isfile(config_file))
+            with open(config_file, 'r') as fo:
+                cfg_dict.update(yaml.safe_load(fo.read()))
+
+        super(YamlParser, self).__init__(cfg_dict)
+
+    
+    def merge_from_file(self, config_file):
+        with open(config_file, 'r') as fo:
+            self.update(yaml.safe_load(fo.read()))
+
+    
+    def merge_from_dict(self, config_dict):
+        self.update(config_dict)
+
+
+def get_config(config_file=None):
+    return YamlParser(config_file=config_file)
+
+
+if __name__ == "__main__":
+    cfg = YamlParser(config_file="../configs/yolov3.yaml")
+    cfg.merge_from_file("../configs/deep_sort.yaml")
+
+    import ipdb; ipdb.set_trace()

deep_sort/utils/tools.py

@@ -0,0 +1,39 @@
+from functools import wraps
+from time import time
+
+
+def is_video(ext: str):
+    """
+    Returns true if ext exists in
+    allowed_exts for video files.
+
+    Args:
+        ext:
+
+    Returns:
+
+    """
+
+    allowed_exts = ('.mp4', '.webm', '.ogg', '.avi', '.wmv', '.mkv', '.3gp')
+    return any((ext.endswith(x) for x in allowed_exts))
+
+
+def tik_tok(func):
+    """
+    keep track of time for each process.
+    Args:
+        func:
+
+    Returns:
+
+    """
+    @wraps(func)
+    def _time_it(*args, **kwargs):
+        start = time()
+        try:
+            return func(*args, **kwargs)
+        finally:
+            end_ = time()
+            print("time: {:.03f}s, fps: {:.03f}".format(end_ - start, 1 / (end_ - start)))
+
+    return _time_it

main.py

@@ -17,7 +17,7 @@ from starlette.middleware.cors import CORSMiddleware
 from application import settings
 from application import urls
 from starlette.staticfiles import StaticFiles  # 依赖安装：pip install aiofiles
-from core.docs import custom_api_docs
+from core.websocket_app import websocket_config
 from core.exception import register_exception
 import typer
 from scripts.initialize.initialize import InitializeData, Environment
@@ -64,7 +64,9 @@ def create_app():
     for url in urls.urlpatterns:
         app.include_router(url["ApiRouter"], prefix=url["prefix"], tags=url["tags"])
     # 配置接口文档静态资源
-    custom_api_docs(app)
+    # custom_api_docs(app)
+    # 开启websocket
+    websocket_config(app)
     return app
 
 

utils/websocket_server.py

@@ -0,0 +1,47 @@
+from fastapi import WebSocket
+
+
+class SocketManager:
+    def __init__(self):
+        self.rooms = {}
+
+    async def add_to_room(self, room: str, websocket: WebSocket):
+        if room not in self.rooms:
+            self.rooms[room] = []
+        self.rooms[room].append(websocket)
+
+    async def remove_from_room(self, room: str, websocket: WebSocket):
+        if room in self.rooms:
+            self.rooms[room].remove(websocket)
+            if len(self.rooms[room]) == 0:
+                del self.rooms[room]
+                if room.startswith('detect_rtsp_'):
+                    print()
+
+    async def broadcast_to_room(self, room: str, message: str, exclude_websocket: WebSocket = None):
+        if room in self.rooms:
+            for ws in self.rooms[room]:
+                if ws != exclude_websocket:
+                    try:
+                        await ws.send_text(message)
+                    except:
+                        await self.remove_from_room(room, ws)
+
+    async def send_to_room(self, room: str, message: str):
+        if room in self.rooms:
+            for ws in self.rooms[room]:
+                try:
+                    await ws.send_text(message)
+                except Exception as e:
+                    print(e)
+
+    async def send_stream_to_room(self, room: str, message):
+        if room in self.rooms:
+            for ws in self.rooms[room]:
+                try:
+                    await ws.send_bytes(message)
+                except Exception as e:
+                    print(e)
+
+
+room_manager = SocketManager()

yolov5/.dockerignore

@@ -0,0 +1,222 @@
+# Repo-specific DockerIgnore -------------------------------------------------------------------------------------------
+.git
+.cache
+.idea
+runs
+output
+coco
+storage.googleapis.com
+
+data/samples/*
+**/results*.csv
+*.jpg
+
+# Neural Network weights -----------------------------------------------------------------------------------------------
+**/*.pt
+**/*.pth
+**/*.onnx
+**/*.engine
+**/*.mlmodel
+**/*.torchscript
+**/*.torchscript.pt
+**/*.tflite
+**/*.h5
+**/*.pb
+*_saved_model/
+*_web_model/
+*_openvino_model/
+
+# Below Copied From .gitignore -----------------------------------------------------------------------------------------
+# Below Copied From .gitignore -----------------------------------------------------------------------------------------
+
+
+# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+wandb/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv*
+venv*/
+ENV*/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+
+# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
+
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+Icon?
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+
+# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff:
+.idea/*
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/dictionaries
+.html  # Bokeh Plots
+.pg  # TensorFlow Frozen Graphs
+.avi # videos
+
+# Sensitive or high-churn files:
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+
+# Gradle:
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# CMake
+cmake-build-debug/
+cmake-build-release/
+
+# Mongo Explorer plugin:
+.idea/**/mongoSettings.xml
+
+## File-based project format:
+*.iws
+
+## Plugin-specific files:
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties

yolov5/.gitattributes

@@ -0,0 +1,2 @@
+# this drop notebooks from GitHub language stats
+*.ipynb linguist-vendored

yolov5/.gitignore

@@ -0,0 +1,258 @@
+# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
+*.jpg
+*.jpeg
+*.png
+*.bmp
+*.tif
+*.tiff
+*.heic
+*.JPG
+*.JPEG
+*.PNG
+*.BMP
+*.TIF
+*.TIFF
+*.HEIC
+*.mp4
+*.mov
+*.MOV
+*.avi
+*.data
+*.json
+*.cfg
+!setup.cfg
+!cfg/yolov3*.cfg
+
+storage.googleapis.com
+runs/*
+data/*
+data/images/*
+!data/*.yaml
+!data/hyps
+!data/scripts
+!data/images
+!data/images/zidane.jpg
+!data/images/bus.jpg
+!data/*.sh
+
+results*.csv
+
+# Datasets -------------------------------------------------------------------------------------------------------------
+coco/
+coco128/
+VOC/
+
+# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
+*.m~
+*.mat
+!targets*.mat
+
+# Neural Network weights -----------------------------------------------------------------------------------------------
+*.weights
+*.pt
+*.pb
+*.onnx
+*.engine
+*.mlmodel
+*.mlpackage
+*.torchscript
+*.tflite
+*.h5
+*_saved_model/
+*_web_model/
+*_openvino_model/
+*_paddle_model/
+darknet53.conv.74
+yolov3-tiny.conv.15
+
+# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+/wandb/
+.installed.cfg
+*.egg
+
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv*
+venv*/
+ENV*/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+
+# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
+
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+Icon?
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+
+# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff:
+.idea/*
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/dictionaries
+.html  # Bokeh Plots
+.pg  # TensorFlow Frozen Graphs
+.avi # videos
+
+# Sensitive or high-churn files:
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+
+# Gradle:
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# CMake
+cmake-build-debug/
+cmake-build-release/
+
+# Mongo Explorer plugin:
+.idea/**/mongoSettings.xml
+
+## File-based project format:
+*.iws
+
+## Plugin-specific files:
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties

yolov5/CITATION.cff

@@ -0,0 +1,14 @@
+cff-version: 1.2.0
+preferred-citation:
+  type: software
+  message: If you use YOLOv5, please cite it as below.
+  authors:
+  - family-names: Jocher
+    given-names: Glenn
+    orcid: "https://orcid.org/0000-0001-5950-6979"
+  title: "YOLOv5 by Ultralytics"
+  version: 7.0
+  doi: 10.5281/zenodo.3908559
+  date-released: 2020-5-29
+  license: AGPL-3.0
+  url: "https://github.com/ultralytics/yolov5"

yolov5/CONTRIBUTING.md

@@ -0,0 +1,76 @@
+## Contributing to YOLOv5 🚀
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's:
+
+- Reporting a bug
+- Discussing the current state of the code
+- Submitting a fix
+- Proposing a new feature
+- Becoming a maintainer
+
+YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be helping push the frontiers of what's possible in AI 😃!
+
+## Submitting a Pull Request (PR) 🛠️
+
+Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps:
+
+### 1. Select File to Update
+
+Select `requirements.txt` to update by clicking on it in GitHub.
+
+<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
+
+### 2. Click 'Edit this file'
+
+The button is in the top-right corner.
+
+<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
+
+### 3. Make Changes
+
+Change the `matplotlib` version from `3.2.2` to `3.3`.
+
+<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
+
+### 4. Preview Changes and Submit PR
+
+Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃!
+
+<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
+
+### PR recommendations
+
+To allow your work to be integrated as seamlessly as possible, we advise you to:
+
+- ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.
+
+<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 15" src="https://user-images.githubusercontent.com/26833433/187295893-50ed9f44-b2c9-4138-a614-de69bd1753d7.png"></p>
+
+- ✅ Verify all YOLOv5 Continuous Integration (CI) **checks are passing**.
+
+<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 03" src="https://user-images.githubusercontent.com/26833433/187296922-545c5498-f64a-4d8c-8300-5fa764360da6.png"></p>
+
+- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee
+
+## Submitting a Bug Report 🐛
+
+If you spot a problem with YOLOv5 please submit a Bug Report!
+
+For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few short guidelines below to help users provide what we need to get started.
+
+When asking a question, people will be better able to provide help if you provide **code** that they can easily understand and use to **reproduce** the problem. This is referred to by community members as creating a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/). Your code that reproduces the problem should be:
+
+- ✅ **Minimal** – Use as little code as possible that still produces the same problem
+- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
+- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
+
+In addition to the above requirements, for [Ultralytics](https://www.ultralytics.com/) to provide assistance your code should be:
+
+- ✅ **Current** – Verify that your code is up-to-date with the current GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new copy to ensure your problem has not already been resolved by previous commits.
+- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://www.ultralytics.com/) does not provide support for custom code ⚠️.
+
+If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us better understand and diagnose your problem.
+
+## License
+
+By contributing, you agree that your contributions will be licensed under the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/)

yolov5/LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
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+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

yolov5/README.md

@@ -0,0 +1,470 @@
+<div align="center">
+  <p>
+    <a href="https://www.ultralytics.com/events/yolovision" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
+  </p>
+
+[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
+
+<div>
+    <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
+    <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+    <a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
+    <br>
+    <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
+    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+    <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+  </div>
+  <br>
+
+YOLOv5 🚀 is the world's most loved vision AI, representing <a href="https://www.ultralytics.com/">Ultralytics</a> open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
+
+We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 <a href="https://docs.ultralytics.com/yolov5/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.com/invite/ultralytics">Discord</a> community for questions and discussions!
+
+To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
+
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
+</div>
+
+</div>
+<br>
+
+## <div align="center">YOLO11 🚀 NEW</div>
+
+We are excited to unveil the launch of Ultralytics YOLO11 🚀, the latest advancement in our state-of-the-art (SOTA) vision models! Available now at **[GitHub](https://github.com/ultralytics/ultralytics)**, YOLO11 builds on our legacy of speed, precision, and ease of use. Whether you're tackling object detection, image segmentation, or image classification, YOLO11 delivers the performance and versatility needed to excel in diverse applications.
+
+Get started today and unlock the full potential of YOLO11! Visit the [Ultralytics Docs](https://docs.ultralytics.com/) for comprehensive guides and resources:
+
+[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://www.pepy.tech/projects/ultralytics)
+
+```bash
+pip install ultralytics
+```
+
+<div align="center">
+  <a href="https://www.ultralytics.com/yolo" target="_blank">
+  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png"></a>
+</div>
+
+## <div align="center">Documentation</div>
+
+See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for full documentation on training, testing and deployment. See below for quickstart examples.
+
+<details open>
+<summary>Install</summary>
+
+Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
+
+```bash
+git clone https://github.com/ultralytics/yolov5  # clone
+cd yolov5
+pip install -r requirements.txt  # install
+```
+
+</details>
+
+<details>
+<summary>Inference</summary>
+
+YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
+
+```python
+import torch
+
+# Model
+model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # or yolov5n - yolov5x6, custom
+
+# Images
+img = "https://ultralytics.com/images/zidane.jpg"  # or file, Path, PIL, OpenCV, numpy, list
+
+# Inference
+results = model(img)
+
+# Results
+results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
+```
+
+</details>
+
+<details>
+<summary>Inference with detect.py</summary>
+
+`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
+
+```bash
+python detect.py --weights yolov5s.pt --source 0                               # webcam
+                                               img.jpg                         # image
+                                               vid.mp4                         # video
+                                               screen                          # screenshot
+                                               path/                           # directory
+                                               list.txt                        # list of images
+                                               list.streams                    # list of streams
+                                               'path/*.jpg'                    # glob
+                                               'https://youtu.be/LNwODJXcvt4'  # YouTube
+                                               'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+```
+
+</details>
+
+<details>
+<summary>Training</summary>
+
+The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
+
+```bash
+python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128
+                                                                 yolov5s                    64
+                                                                 yolov5m                    40
+                                                                 yolov5l                    24
+                                                                 yolov5x                    16
+```
+
+<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
+
+</details>
+
+<details open>
+<summary>Tutorials</summary>
+
+- [Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) 🚀 RECOMMENDED
+- [Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/) ☘️
+- [Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)
+- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 🌟 NEW
+- [TFLite, ONNX, CoreML, TensorRT Export](https://docs.ultralytics.com/yolov5/tutorials/model_export/) 🚀
+- [NVIDIA Jetson platform Deployment](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano/) 🌟 NEW
+- [Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)
+- [Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)
+- [Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)
+- [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
+- [Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)
+- [Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/) 🌟 NEW
+- [Ultralytics HUB to train and deploy YOLO](https://www.ultralytics.com/hub) 🚀 RECOMMENDED
+- [ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)
+- [YOLOv5 with Neural Magic's Deepsparse](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)
+- [Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/) 🌟 NEW
+
+</details>
+
+## <div align="center">Integrations</div>
+
+Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [W&B](https://docs.wandb.ai/guides/integrations/ultralytics/), [Comet](https://bit.ly/yolov8-readme-comet), [Roboflow](https://roboflow.com/?ref=ultralytics) and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.
+
+<br>
+<a href="https://www.ultralytics.com/hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a>
+<br>
+<br>
+
+<div align="center">
+  <a href="https://www.ultralytics.com/hub">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://docs.wandb.ai/guides/integrations/ultralytics/">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="ClearML logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://bit.ly/yolov8-readme-comet">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://bit.ly/yolov5-neuralmagic">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
+</div>
+
+|                                                         Ultralytics HUB 🚀                                                         |                                                               W&B                                                               |                                                                       Comet ⭐ NEW                                                                        |                                              Neural Magic                                              |
+| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
+| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://www.ultralytics.com/hub). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLOv5 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
+
+## <div align="center">Ultralytics HUB</div>
+
+Experience seamless AI with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** now!
+
+<a align="center" href="https://www.ultralytics.com/hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
+
+## <div align="center">Why YOLOv5</div>
+
+YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results.
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
+<details>
+  <summary>YOLOv5-P5 640 Figure</summary>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
+</details>
+<details>
+  <summary>Figure Notes</summary>
+
+- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
+- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p4/) V100 instance at batch-size 32.
+- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
+- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+
+</details>
+
+### Pretrained Checkpoints
+
+| Model                                                                                           | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
+| ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
+| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt)              | 640                   | 28.0                 | 45.7              | **45**                       | **6.3**                       | **0.6**                        | **1.9**            | **4.5**                |
+| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt)              | 640                   | 37.4                 | 56.8              | 98                           | 6.4                           | 0.9                            | 7.2                | 16.5                   |
+| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt)              | 640                   | 45.4                 | 64.1              | 224                          | 8.2                           | 1.7                            | 21.2               | 49.0                   |
+| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt)              | 640                   | 49.0                 | 67.3              | 430                          | 10.1                          | 2.7                            | 46.5               | 109.1                  |
+| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt)              | 640                   | 50.7                 | 68.9              | 766                          | 12.1                          | 4.8                            | 86.7               | 205.7                  |
+|                                                                                                 |                       |                      |                   |                              |                               |                                |                    |                        |
+| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt)            | 1280                  | 36.0                 | 54.4              | 153                          | 8.1                           | 2.1                            | 3.2                | 4.6                    |
+| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt)            | 1280                  | 44.8                 | 63.7              | 385                          | 8.2                           | 3.6                            | 12.6               | 16.8                   |
+| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt)            | 1280                  | 51.3                 | 69.3              | 887                          | 11.1                          | 6.8                            | 35.7               | 50.0                   |
+| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt)            | 1280                  | 53.7                 | 71.3              | 1784                         | 15.8                          | 10.5                           | 76.8               | 111.4                  |
+| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [TTA] | 1280<br>1536          | 55.0<br>**55.8**     | 72.7<br>**72.7**  | 3136<br>-                    | 26.2<br>-                     | 19.4<br>-                      | 140.7<br>-         | 209.8<br>-             |
+
+<details>
+  <summary>Table Notes</summary>
+
+- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p4/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
+- **TTA** [Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+
+</details>
+
+## <div align="center">Segmentation</div>
+
+Our new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials.
+
+<details>
+  <summary>Segmentation Checkpoints</summary>
+
+<div align="center">
+<a align="center" href="https://www.ultralytics.com/yolo" target="_blank">
+<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a>
+</div>
+
+We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for easy reproducibility.
+
+| Model                                                                                      | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Train time<br><sup>300 epochs<br>A100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TRT A100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
+| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- |
+| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640                   | 27.6                 | 23.4                  | 80:17                                         | **62.7**                       | **1.2**                        | **2.0**            | **7.1**                |
+| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640                   | 37.6                 | 31.7                  | 88:16                                         | 173.3                          | 1.4                            | 7.6                | 26.4                   |
+| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640                   | 45.0                 | 37.1                  | 108:36                                        | 427.0                          | 2.2                            | 22.0               | 70.8                   |
+| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640                   | 49.0                 | 39.9                  | 66:43 (2x)                                    | 857.4                          | 2.9                            | 47.9               | 147.7                  |
+| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640                   | **50.7**             | **41.4**              | 62:56 (3x)                                    | 1579.2                         | 4.5                            | 88.8               | 265.7                  |
+
+- All checkpoints are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official
+- **Accuracy** values are for single-model single-scale on COCO dataset.<br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
+- **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image). <br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
+- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
+
+</details>
+
+<details>
+  <summary>Segmentation Usage Examples &nbsp;<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
+
+### Train
+
+YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with `--data coco128-seg.yaml` argument and manual download of COCO-segments dataset with `bash data/scripts/get_coco.sh --train --val --segments` and then `python train.py --data coco.yaml`.
+
+```bash
+# Single-GPU
+python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
+
+# Multi-GPU DDP
+python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
+```
+
+### Val
+
+Validate YOLOv5s-seg mask mAP on COCO dataset:
+
+```bash
+bash data/scripts/get_coco.sh --val --segments  # download COCO val segments split (780MB, 5000 images)
+python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640  # validate
+```
+
+### Predict
+
+Use pretrained YOLOv5m-seg.pt to predict bus.jpg:
+
+```bash
+python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
+```
+
+```python
+model = torch.hub.load(
+    "ultralytics/yolov5", "custom", "yolov5m-seg.pt"
+)  # load from PyTorch Hub (WARNING: inference not yet supported)
+```
+
+| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |
+| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
+
+### Export
+
+Export YOLOv5s-seg model to ONNX and TensorRT:
+
+```bash
+python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
+```
+
+</details>
+
+## <div align="center">Classification</div>
+
+YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation and deployment! See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) and visit our [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart tutorials.
+
+<details>
+  <summary>Classification Checkpoints</summary>
+
+<br>
+
+We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility.
+
+| Model                                                                                              | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Training<br><sup>90 epochs<br>4xA100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TensorRT V100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@224 (B) |
+| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- |
+| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt)         | 224                   | 64.6             | 85.4             | 7:59                                         | **3.3**                        | **0.5**                             | **2.5**            | **0.5**                |
+| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt)         | 224                   | 71.5             | 90.2             | 8:09                                         | 6.6                            | 0.6                                 | 5.4                | 1.4                    |
+| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt)         | 224                   | 75.9             | 92.9             | 10:06                                        | 15.5                           | 0.9                                 | 12.9               | 3.9                    |
+| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt)         | 224                   | 78.0             | 94.0             | 11:56                                        | 26.9                           | 1.4                                 | 26.5               | 8.5                    |
+| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt)         | 224                   | **79.0**         | **94.4**         | 15:04                                        | 54.3                           | 1.8                                 | 48.1               | 15.9                   |
+|                                                                                                    |                       |                  |                  |                                              |                                |                                     |                    |                        |
+| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt)               | 224                   | 70.3             | 89.5             | **6:47**                                     | 11.2                           | 0.5                                 | 11.7               | 3.7                    |
+| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt)               | 224                   | 73.9             | 91.8             | 8:33                                         | 20.6                           | 0.9                                 | 21.8               | 7.4                    |
+| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt)               | 224                   | 76.8             | 93.4             | 11:10                                        | 23.4                           | 1.0                                 | 25.6               | 8.5                    |
+| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt)             | 224                   | 78.5             | 94.3             | 17:10                                        | 42.1                           | 1.9                                 | 44.5               | 15.9                   |
+|                                                                                                    |                       |                  |                  |                                              |                                |                                     |                    |                        |
+| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224                   | 75.1             | 92.4             | 13:03                                        | 12.5                           | 1.3                                 | 5.3                | 1.0                    |
+| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224                   | 76.4             | 93.2             | 17:04                                        | 14.9                           | 1.6                                 | 7.8                | 1.5                    |
+| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224                   | 76.6             | 93.4             | 17:10                                        | 15.9                           | 1.6                                 | 9.1                | 1.7                    |
+| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224                   | 77.7             | 94.0             | 19:19                                        | 18.9                           | 1.9                                 | 12.2               | 2.4                    |
+
+<details>
+  <summary>Table Notes (click to expand)</summary>
+
+- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
+- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
+- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
+- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
+
+</details>
+</details>
+
+<details>
+  <summary>Classification Usage Examples &nbsp;<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
+
+### Train
+
+YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.
+
+```bash
+# Single-GPU
+python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
+
+# Multi-GPU DDP
+python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+```
+
+### Val
+
+Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
+
+```bash
+bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
+```
+
+### Predict
+
+Use pretrained YOLOv5s-cls.pt to predict bus.jpg:
+
+```bash
+python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
+```
+
+```python
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")  # load from PyTorch Hub
+```
+
+### Export
+
+Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:
+
+```bash
+python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
+```
+
+</details>
+
+## <div align="center">Environments</div>
+
+Get started in seconds with our verified environments. Click each icon below for details.
+
+<div align="center">
+  <a href="https://bit.ly/yolov5-paperspace-notebook">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://www.kaggle.com/models/ultralytics/yolov5">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://hub.docker.com/r/ultralytics/yolov5">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png" width="10%" /></a>
+</div>
+
+## <div align="center">Contribute</div>
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out the [YOLOv5 Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+
+<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
+<img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a>
+
+## <div align="center">License</div>
+
+Ultralytics offers two licensing options to accommodate diverse use cases:
+
+- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for more details.
+- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://www.ultralytics.com/license).
+
+## <div align="center">Contact</div>
+
+For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues), and join our [Discord](https://discord.com/invite/ultralytics) community for questions and discussions!
+
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+</div>
+
+[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation

yolov5/README.zh-CN.md

@@ -0,0 +1,470 @@
+<div align="center">
+  <p>
+    <a href="https://www.ultralytics.com/events/yolovision" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
+  </p>
+
+[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
+
+<div>
+    <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
+    <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+    <a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
+    <br>
+    <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
+    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+    <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+  </div>
+  <br>
+
+YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表<a href="https://www.ultralytics.com/"> Ultralytics </a>对未来视觉 AI 方法的开源研究，结合在数千小时的研究和开发中积累的经验教训和最佳实践。
+
+我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 YOLOv5 <a href="https://docs.ultralytics.com/yolov5/">文档</a> 了解详细信息，在 <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> 上提交问题以获得支持，并加入我们的 <a href="https://discord.com/invite/ultralytics">Discord</a> 社区进行问题和讨论！
+
+如需申请企业许可，请在 [Ultralytics Licensing](https://www.ultralytics.com/license) 处填写表格
+
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
+</div>
+</div>
+
+## <div align="center">YOLO11 🚀 全新发布</div>
+
+我们很高兴宣布推出 Ultralytics YOLO11 🚀，这是我们最先进视觉模型的最新进展！现已在 **[GitHub](https://github.com/ultralytics/ultralytics)** 上发布。YOLO11 在速度、精度和易用性方面进一步提升，无论是处理目标检测、图像分割还是图像分类任务，YOLO11 都具备出色的性能和多功能性，助您在各种应用中脱颖而出。
+
+立即开始，解锁 YOLO11 的全部潜力！访问 [Ultralytics 文档](https://docs.ultralytics.com/) 获取全面的指南和资源：
+
+[![PyPI 版本](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![下载量](https://static.pepy.tech/badge/ultralytics)](https://www.pepy.tech/projects/ultralytics)
+
+```bash
+pip install ultralytics
+```
+
+<div align="center">
+  <a href="https://www.ultralytics.com/yolo" target="_blank">
+  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png"></a>
+</div>
+
+## <div align="center">文档</div>
+
+有关训练、测试和部署的完整文档见[YOLOv5 文档](https://docs.ultralytics.com/yolov5/)。请参阅下面的快速入门示例。
+
+<details open>
+<summary>安装</summary>
+
+克隆 repo，并要求在 [**Python>=3.8.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) 。
+
+```bash
+git clone https://github.com/ultralytics/yolov5  # clone
+cd yolov5
+pip install -r requirements.txt  # install
+```
+
+</details>
+
+<details>
+<summary>推理</summary>
+
+使用 YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。
+
+```python
+import torch
+
+# Model
+model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # or yolov5n - yolov5x6, custom
+
+# Images
+img = "https://ultralytics.com/images/zidane.jpg"  # or file, Path, PIL, OpenCV, numpy, list
+
+# Inference
+results = model(img)
+
+# Results
+results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
+```
+
+</details>
+
+<details>
+<summary>使用 detect.py 推理</summary>
+
+`detect.py` 在各种来源上运行推理， [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载，并将结果保存到 `runs/detect` 。
+
+```bash
+python detect.py --weights yolov5s.pt --source 0                               # webcam
+                                               img.jpg                         # image
+                                               vid.mp4                         # video
+                                               screen                          # screenshot
+                                               path/                           # directory
+                                               list.txt                        # list of images
+                                               list.streams                    # list of streams
+                                               'path/*.jpg'                    # glob
+                                               'https://youtu.be/LNwODJXcvt4'  # YouTube
+                                               'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+```
+
+</details>
+
+<details>
+<summary>训练</summary>
+
+下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data)
+将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/) 训练速度更快）。 尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。
+
+```bash
+python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128
+                                                                 yolov5s                    64
+                                                                 yolov5m                    40
+                                                                 yolov5l                    24
+                                                                 yolov5x                    16
+```
+
+<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
+
+</details>
+
+<details open>
+<summary>教程</summary>
+
+- [自定义数据训练](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) 🚀 **推荐**
+- [最佳训练效果的提示](https://docs.ultralytics.com/guides/model-training-tips/) ☘️
+- [多GPU训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)
+- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 🌟 **全新**
+- [TFLite, ONNX, CoreML, TensorRT 导出](https://docs.ultralytics.com/yolov5/tutorials/model_export/) 🚀
+- [NVIDIA Jetson 平台部署](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano/) 🌟 **全新**
+- [测试时增强 (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)
+- [模型集成](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)
+- [模型剪枝/稀疏化](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)
+- [超参数进化](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
+- [冻结层的迁移学习](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)
+- [架构概述](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/) 🌟 **全新**
+- [使用 Ultralytics HUB 进行 YOLO 训练和部署](https://www.ultralytics.com/hub) 🚀 **推荐**
+- [ClearML 日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)
+- [与 Neural Magic 的 Deepsparse 集成的 YOLOv5](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)
+- [Comet 日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/) 🌟 **全新**
+
+</details>
+
+## <div align="center">集成</div>
+
+我们与领先的 AI 平台的关键集成扩展了 Ultralytics 产品的功能，提升了数据集标注、训练、可视化和模型管理等任务。探索 Ultralytics 如何通过与 [W&B](https://docs.wandb.ai/guides/integrations/ultralytics/)、[Comet](https://bit.ly/yolov8-readme-comet)、[Roboflow](https://roboflow.com/?ref=ultralytics) 和 [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 的合作，优化您的 AI 工作流程。
+
+<br>
+<a href="https://www.ultralytics.com/hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a>
+<br>
+<br>
+
+<div align="center">
+  <a href="https://www.ultralytics.com/hub">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://docs.wandb.ai/guides/integrations/ultralytics/">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="W&B logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://bit.ly/yolov8-readme-comet">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
+  <a href="https://bit.ly/yolov5-neuralmagic">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
+</div>
+
+|                                              Ultralytics HUB 🚀                                              |                                                  W&B                                                   |                                                     Comet ⭐ 全新                                                      |                                            Neural Magic                                             |
+| :----------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: |
+| 简化 YOLO 工作流程：通过 [Ultralytics HUB](https://www.ultralytics.com/hub) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) 跟踪实验、超参数和结果 | 永久免费，[Comet](https://bit.ly/yolov5-readme-comet) 允许您保存 YOLO11 模型、恢复训练，并交互式地可视化和调试预测结果 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 运行 YOLO11 推理，速度提升至 6 倍 |
+
+## <div align="center">Ultralytics HUB</div>
+
+[Ultralytics HUB](https://www.ultralytics.com/hub) 是我们的⭐**新的**用于可视化数据集、训练 YOLOv5 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 **免费** 使用他！
+
+<a align="center" href="https://www.ultralytics.com/hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
+
+## <div align="center">为什么选择 YOLOv5</div>
+
+YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结果。
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
+<details>
+  <summary>YOLOv5-P5 640 图</summary>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
+</details>
+<details>
+  <summary>图表笔记</summary>
+
+- **COCO AP val** 表示 mAP@0.5:0.95 指标，在 [COCO val2017](http://cocodataset.org) 数据集的 5000 张图像上测得， 图像包含 256 到 1536 各种推理大小。
+- **显卡推理速度** 为在 [COCO val2017](http://cocodataset.org) 数据集上的平均推理时间，使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p4/) V100实例，batchsize 为 32 。
+- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ， batchsize 为32。
+- **复现命令** 为 `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+
+</details>
+
+### 预训练模型
+
+| 模型                                                                                           | 尺寸<br><sup>（像素） | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | 推理速度<br><sup>CPU b1<br>（ms） | 推理速度<br><sup>V100 b1<br>（ms） | 速度<br><sup>V100 b32<br>（ms） | 参数量<br><sup>(M) | FLOPs<br><sup>@640 (B) |
+| ---------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | --------------------------------- | ---------------------------------- | ------------------------------- | ------------------ | ---------------------- |
+| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt)             | 640                   | 28.0                 | 45.7              | **45**                            | **6.3**                            | **0.6**                         | **1.9**            | **4.5**                |
+| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt)             | 640                   | 37.4                 | 56.8              | 98                                | 6.4                                | 0.9                             | 7.2                | 16.5                   |
+| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt)             | 640                   | 45.4                 | 64.1              | 224                               | 8.2                                | 1.7                             | 21.2               | 49.0                   |
+| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt)             | 640                   | 49.0                 | 67.3              | 430                               | 10.1                               | 2.7                             | 46.5               | 109.1                  |
+| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt)             | 640                   | 50.7                 | 68.9              | 766                               | 12.1                               | 4.8                             | 86.7               | 205.7                  |
+|                                                                                                |                       |                      |                   |                                   |                                    |                                 |                    |                        |
+| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt)           | 1280                  | 36.0                 | 54.4              | 153                               | 8.1                                | 2.1                             | 3.2                | 4.6                    |
+| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt)           | 1280                  | 44.8                 | 63.7              | 385                               | 8.2                                | 3.6                             | 12.6               | 16.8                   |
+| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt)           | 1280                  | 51.3                 | 69.3              | 887                               | 11.1                               | 6.8                             | 35.7               | 50.0                   |
+| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt)           | 1280                  | 53.7                 | 71.3              | 1784                              | 15.8                               | 10.5                            | 76.8               | 111.4                  |
+| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+[TTA] | 1280<br>1536          | 55.0<br>**55.8**     | 72.7<br>**72.7**  | 3136<br>-                         | 26.2<br>-                          | 19.4<br>-                       | 140.7<br>-         | 209.8<br>-             |
+
+<details>
+  <summary>笔记</summary>
+
+- 所有模型都使用默认配置，训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ，其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。
+- \*\*mAP<sup>val</sup>\*\*在单模型单尺度上计算，数据集使用 [COCO val2017](http://cocodataset.org) 。<br>复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+- **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p4/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。<br>复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1`
+- **TTA** [测试时数据增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) 包括反射和尺度变换。<br>复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+
+</details>
+
+## <div align="center">实例分割模型 ⭐ 新</div>
+
+我们新的 YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) 实例分割模型是世界上最快和最准确的模型，击败所有当前 [SOTA 基准](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco)。我们使它非常易于训练、验证和部署。更多细节请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v7.0) 或访问我们的 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) 以快速入门。
+
+<details>
+  <summary>实例分割模型列表</summary>
+
+<br>
+
+<div align="center">
+<a align="center" href="https://www.ultralytics.com/yolo" target="_blank">
+<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a>
+</div>
+
+我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 epochs 得到 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于再现，我们在 Google [Colab Pro](https://colab.research.google.com/signup) 上进行了所有速度测试。
+
+| 模型                                                                                       | 尺寸<br><sup>（像素） | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 训练时长<br><sup>300 epochs<br>A100 GPU（小时） | 推理速度<br><sup>ONNX CPU<br>（ms） | 推理速度<br><sup>TRT A100<br>（ms） | 参数量<br><sup>(M) | FLOPs<br><sup>@640 (B) |
+| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | ----------------------------------------------- | ----------------------------------- | ----------------------------------- | ------------------ | ---------------------- |
+| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640                   | 27.6                 | 23.4                  | 80:17                                           | **62.7**                            | **1.2**                             | **2.0**            | **7.1**                |
+| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640                   | 37.6                 | 31.7                  | 88:16                                           | 173.3                               | 1.4                                 | 7.6                | 26.4                   |
+| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640                   | 45.0                 | 37.1                  | 108:36                                          | 427.0                               | 2.2                                 | 22.0               | 70.8                   |
+| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640                   | 49.0                 | 39.9                  | 66:43 (2x)                                      | 857.4                               | 2.9                                 | 47.9               | 147.7                  |
+| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640                   | **50.7**             | **41.4**              | 62:56 (3x)                                      | 1579.2                              | 4.5                                 | 88.8               | 265.7                  |
+
+- 所有模型使用 SGD 优化器训练， 都使用 `lr0=0.01` 和 `weight_decay=5e-5` 参数， 图像大小为 640 。<br>训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5_v70_official
+- **准确性**结果都在 COCO 数据集上，使用单模型单尺度测试得到。<br>复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
+- **推理速度**是使用 100 张图像推理时间进行平均得到，测试环境使用 [Colab Pro](https://colab.research.google.com/signup) 上 A100 高 RAM 实例。结果仅表示推理速度（NMS 每张图像增加约 1 毫秒）。<br>复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
+- **模型转换**到 FP32 的 ONNX 和 FP16 的 TensorRT 脚本为 `export.py`.<br>运行命令 `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
+
+</details>
+
+<details>
+  <summary>分割模型使用示例 &nbsp;<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
+
+### 训练
+
+YOLOv5分割训练支持自动下载 COCO128-seg 分割数据集，用户仅需在启动指令中包含 `--data coco128-seg.yaml` 参数。 若要手动下载，使用命令 `bash data/scripts/get_coco.sh --train --val --segments`， 在下载完毕后，使用命令 `python train.py --data coco.yaml` 开启训练。
+
+```bash
+# 单 GPU
+python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
+
+# 多 GPU， DDP 模式
+python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
+```
+
+### 验证
+
+在 COCO 数据集上验证 YOLOv5s-seg mask mAP：
+
+```bash
+bash data/scripts/get_coco.sh --val --segments  # 下载 COCO val segments 数据集 (780MB, 5000 images)
+python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640  # 验证
+```
+
+### 预测
+
+使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg：
+
+```bash
+python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
+```
+
+```python
+model = torch.hub.load(
+    "ultralytics/yolov5", "custom", "yolov5m-seg.pt"
+)  # 从load from PyTorch Hub 加载模型 (WARNING: 推理暂未支持)
+```
+
+| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |
+| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
+
+### 模型导出
+
+将 YOLOv5s-seg 模型导出到 ONNX 和 TensorRT：
+
+```bash
+python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
+```
+
+</details>
+
+## <div align="center">分类网络 ⭐ 新</div>
+
+YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) 带来对分类模型训练、验证和部署的支持！详情请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v6.2) 或访问我们的 [YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) 以快速入门。
+
+<details>
+  <summary>分类网络模型</summary>
+
+<br>
+
+我们使用 4xA100 实例在 ImageNet 上训练了 90 个 epochs 得到 YOLOv5-cls 分类模型，我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于重现，我们在 Google 上进行了所有速度测试 [Colab Pro](https://colab.research.google.com/signup) 。
+
+| 模型                                                                                               | 尺寸<br><sup>（像素） | acc<br><sup>top1 | acc<br><sup>top5 | 训练时长<br><sup>90 epochs<br>4xA100（小时） | 推理速度<br><sup>ONNX CPU<br>（ms） | 推理速度<br><sup>TensorRT V100<br>（ms） | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
+| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ----------------------------------- | ---------------------------------------- | ---------------- | ---------------------- |
+| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt)         | 224                   | 64.6             | 85.4             | 7:59                                         | **3.3**                             | **0.5**                                  | **2.5**          | **0.5**                |
+| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt)         | 224                   | 71.5             | 90.2             | 8:09                                         | 6.6                                 | 0.6                                      | 5.4              | 1.4                    |
+| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt)         | 224                   | 75.9             | 92.9             | 10:06                                        | 15.5                                | 0.9                                      | 12.9             | 3.9                    |
+| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt)         | 224                   | 78.0             | 94.0             | 11:56                                        | 26.9                                | 1.4                                      | 26.5             | 8.5                    |
+| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt)         | 224                   | **79.0**         | **94.4**         | 15:04                                        | 54.3                                | 1.8                                      | 48.1             | 15.9                   |
+|                                                                                                    |                       |                  |                  |                                              |                                     |                                          |                  |                        |
+| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt)               | 224                   | 70.3             | 89.5             | **6:47**                                     | 11.2                                | 0.5                                      | 11.7             | 3.7                    |
+| [Resnetzch](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt)              | 224                   | 73.9             | 91.8             | 8:33                                         | 20.6                                | 0.9                                      | 21.8             | 7.4                    |
+| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt)               | 224                   | 76.8             | 93.4             | 11:10                                        | 23.4                                | 1.0                                      | 25.6             | 8.5                    |
+| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt)             | 224                   | 78.5             | 94.3             | 17:10                                        | 42.1                                | 1.9                                      | 44.5             | 15.9                   |
+|                                                                                                    |                       |                  |                  |                                              |                                     |                                          |                  |                        |
+| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224                   | 75.1             | 92.4             | 13:03                                        | 12.5                                | 1.3                                      | 5.3              | 1.0                    |
+| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224                   | 76.4             | 93.2             | 17:04                                        | 14.9                                | 1.6                                      | 7.8              | 1.5                    |
+| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224                   | 76.6             | 93.4             | 17:10                                        | 15.9                                | 1.6                                      | 9.1              | 1.7                    |
+| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224                   | 77.7             | 94.0             | 19:19                                        | 18.9                                | 1.9                                      | 12.2             | 2.4                    |
+
+<details>
+  <summary>Table Notes (点击以展开)</summary>
+
+- 所有模型都使用 SGD 优化器训练 90 个 epochs，都使用 `lr0=0.001` 和 `weight_decay=5e-5` 参数， 图像大小为 224 ，且都使用默认设置。<br>训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
+- **准确性**都在单模型单尺度上计算，数据集使用 [ImageNet-1k](https://www.image-net.org/index.php) 。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224`
+- **推理速度**是使用 100 个推理图像进行平均得到，测试环境使用谷歌 [Colab Pro](https://colab.research.google.com/signup) V100 高 RAM 实例。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
+- **模型导出**到 FP32 的 ONNX 和 FP16 的 TensorRT 使用 `export.py` 。<br>复现命令 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
+</details>
+</details>
+
+<details>
+  <summary>分类训练示例 &nbsp;<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
+
+### 训练
+
+YOLOv5 分类训练支持自动下载 MNIST、Fashion-MNIST、CIFAR10、CIFAR100、Imagenette、Imagewoof 和 ImageNet 数据集，命令中使用 `--data` 即可。 MNIST 示例 `--data mnist` 。
+
+```bash
+# 单 GPU
+python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
+
+# 多 GPU， DDP 模式
+python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+```
+
+### 验证
+
+在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性：
+
+```bash
+bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
+```
+
+### 预测
+
+使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg：
+
+```bash
+python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
+```
+
+```python
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")  # load from PyTorch Hub
+```
+
+### 模型导出
+
+将一组经过训练的 YOLOv5s-cls、ResNet 和 EfficientNet 模型导出到 ONNX 和 TensorRT：
+
+```bash
+python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
+```
+
+</details>
+
+## <div align="center">环境</div>
+
+使用下面我们经过验证的环境，在几秒钟内开始使用 YOLOv5 。单击下面的图标了解详细信息。
+
+<div align="center">
+  <a href="https://bit.ly/yolov5-paperspace-notebook">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://www.kaggle.com/models/ultralytics/yolov5">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://hub.docker.com/r/ultralytics/yolov5">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png" width="10%" /></a>
+</div>
+
+## <div align="center">贡献</div>
+
+我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的 [投稿指南](https://docs.ultralytics.com/help/contributing/)，并填写 [YOLOv5调查](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者！
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+
+<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
+<img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a>
+
+## <div align="center">许可证</div>
+
+Ultralytics 提供两种许可证选项以适应各种使用场景：
+
+- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/license)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件以了解更多细节。
+- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://www.ultralytics.com/license)与我们联系。
+
+## <div align="center">联系方式</div>
+
+对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)，并加入我们的 [Discord](https://discord.com/invite/ultralytics) 社区进行问题和讨论！
+
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+</div>
+
+[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation

yolov5/benchmarks.py

@@ -0,0 +1,294 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+Run YOLOv5 benchmarks on all supported export formats.
+
+Format                      | `export.py --include`         | Model
+---                         | ---                           | ---
+PyTorch                     | -                             | yolov5s.pt
+TorchScript                 | `torchscript`                 | yolov5s.torchscript
+ONNX                        | `onnx`                        | yolov5s.onnx
+OpenVINO                    | `openvino`                    | yolov5s_openvino_model/
+TensorRT                    | `engine`                      | yolov5s.engine
+CoreML                      | `coreml`                      | yolov5s.mlpackage
+TensorFlow SavedModel       | `saved_model`                 | yolov5s_saved_model/
+TensorFlow GraphDef         | `pb`                          | yolov5s.pb
+TensorFlow Lite             | `tflite`                      | yolov5s.tflite
+TensorFlow Edge TPU         | `edgetpu`                     | yolov5s_edgetpu.tflite
+TensorFlow.js               | `tfjs`                        | yolov5s_web_model/
+
+Requirements:
+    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU
+    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow  # GPU
+    $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com  # TensorRT
+
+Usage:
+    $ python benchmarks.py --weights yolov5s.pt --img 640
+"""
+
+import argparse
+import platform
+import sys
+import time
+from pathlib import Path
+
+import pandas as pd
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+# ROOT = ROOT.relative_to(Path.cwd())  # relative
+
+import export
+from models.experimental import attempt_load
+from models.yolo import SegmentationModel
+from segment.val import run as val_seg
+from utils import notebook_init
+from utils.general import LOGGER, check_yaml, file_size, print_args
+from utils.torch_utils import select_device
+from val import run as val_det
+
+
+def run(
+    weights=ROOT / "yolov5s.pt",  # weights path
+    imgsz=640,  # inference size (pixels)
+    batch_size=1,  # batch size
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    half=False,  # use FP16 half-precision inference
+    test=False,  # test exports only
+    pt_only=False,  # test PyTorch only
+    hard_fail=False,  # throw error on benchmark failure
+):
+    """
+    Run YOLOv5 benchmarks on multiple export formats and log results for model performance evaluation.
+
+    Args:
+        weights (Path | str): Path to the model weights file (default: ROOT / "yolov5s.pt").
+        imgsz (int): Inference size in pixels (default: 640).
+        batch_size (int): Batch size for inference (default: 1).
+        data (Path | str): Path to the dataset.yaml file (default: ROOT / "data/coco128.yaml").
+        device (str): CUDA device, e.g., '0' or '0,1,2,3' or 'cpu' (default: "").
+        half (bool): Use FP16 half-precision inference (default: False).
+        test (bool): Test export formats only (default: False).
+        pt_only (bool): Test PyTorch format only (default: False).
+        hard_fail (bool): Throw an error on benchmark failure if True (default: False).
+
+    Returns:
+        None. Logs information about the benchmark results, including the format, size, mAP50-95, and inference time.
+
+    Notes:
+        Supported export formats and models include PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML,
+            TensorFlow SavedModel, TensorFlow GraphDef, TensorFlow Lite, and TensorFlow Edge TPU. Edge TPU and TF.js
+            are unsupported.
+
+    Example:
+        ```python
+        $ python benchmarks.py --weights yolov5s.pt --img 640
+        ```
+
+    Usage:
+        Install required packages:
+          $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU support
+          $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow   # GPU support
+          $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com  # TensorRT
+
+        Run benchmarks:
+          $ python benchmarks.py --weights yolov5s.pt --img 640
+    """
+    y, t = [], time.time()
+    device = select_device(device)
+    model_type = type(attempt_load(weights, fuse=False))  # DetectionModel, SegmentationModel, etc.
+    for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows():  # index, (name, file, suffix, CPU, GPU)
+        try:
+            assert i not in (9, 10), "inference not supported"  # Edge TPU and TF.js are unsupported
+            assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13"  # CoreML
+            if "cpu" in device.type:
+                assert cpu, "inference not supported on CPU"
+            if "cuda" in device.type:
+                assert gpu, "inference not supported on GPU"
+
+            # Export
+            if f == "-":
+                w = weights  # PyTorch format
+            else:
+                w = export.run(
+                    weights=weights, imgsz=[imgsz], include=[f], batch_size=batch_size, device=device, half=half
+                )[-1]  # all others
+            assert suffix in str(w), "export failed"
+
+            # Validate
+            if model_type == SegmentationModel:
+                result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half)
+                metric = result[0][7]  # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))
+            else:  # DetectionModel:
+                result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half)
+                metric = result[0][3]  # (p, r, map50, map, *loss(box, obj, cls))
+            speed = result[2][1]  # times (preprocess, inference, postprocess)
+            y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)])  # MB, mAP, t_inference
+        except Exception as e:
+            if hard_fail:
+                assert type(e) is AssertionError, f"Benchmark --hard-fail for {name}: {e}"
+            LOGGER.warning(f"WARNING ⚠️ Benchmark failure for {name}: {e}")
+            y.append([name, None, None, None])  # mAP, t_inference
+        if pt_only and i == 0:
+            break  # break after PyTorch
+
+    # Print results
+    LOGGER.info("\n")
+    parse_opt()
+    notebook_init()  # print system info
+    c = ["Format", "Size (MB)", "mAP50-95", "Inference time (ms)"] if map else ["Format", "Export", "", ""]
+    py = pd.DataFrame(y, columns=c)
+    LOGGER.info(f"\nBenchmarks complete ({time.time() - t:.2f}s)")
+    LOGGER.info(str(py if map else py.iloc[:, :2]))
+    if hard_fail and isinstance(hard_fail, str):
+        metrics = py["mAP50-95"].array  # values to compare to floor
+        floor = eval(hard_fail)  # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
+        assert all(x > floor for x in metrics if pd.notna(x)), f"HARD FAIL: mAP50-95 < floor {floor}"
+    return py
+
+
+def test(
+    weights=ROOT / "yolov5s.pt",  # weights path
+    imgsz=640,  # inference size (pixels)
+    batch_size=1,  # batch size
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    half=False,  # use FP16 half-precision inference
+    test=False,  # test exports only
+    pt_only=False,  # test PyTorch only
+    hard_fail=False,  # throw error on benchmark failure
+):
+    """
+    Run YOLOv5 export tests for all supported formats and log the results, including export statuses.
+
+    Args:
+        weights (Path | str): Path to the model weights file (.pt format). Default is 'ROOT / "yolov5s.pt"'.
+        imgsz (int): Inference image size (in pixels). Default is 640.
+        batch_size (int): Batch size for testing. Default is 1.
+        data (Path | str): Path to the dataset configuration file (.yaml format). Default is 'ROOT / "data/coco128.yaml"'.
+        device (str): Device for running the tests, can be 'cpu' or a specific CUDA device ('0', '0,1,2,3', etc.). Default is an empty string.
+        half (bool): Use FP16 half-precision for inference if True. Default is False.
+        test (bool): Test export formats only without running inference. Default is False.
+        pt_only (bool): Test only the PyTorch model if True. Default is False.
+        hard_fail (bool): Raise error on export or test failure if True. Default is False.
+
+    Returns:
+        pd.DataFrame: DataFrame containing the results of the export tests, including format names and export statuses.
+
+    Examples:
+        ```python
+        $ python benchmarks.py --weights yolov5s.pt --img 640
+        ```
+
+    Notes:
+        Supported export formats and models include PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow
+        SavedModel, TensorFlow GraphDef, TensorFlow Lite, and TensorFlow Edge TPU. Edge TPU and TF.js are unsupported.
+
+    Usage:
+        Install required packages:
+            $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU support
+            $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow   # GPU support
+            $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com  # TensorRT
+        Run export tests:
+            $ python benchmarks.py --weights yolov5s.pt --img 640
+    """
+    y, t = [], time.time()
+    device = select_device(device)
+    for i, (name, f, suffix, gpu) in export.export_formats().iterrows():  # index, (name, file, suffix, gpu-capable)
+        try:
+            w = (
+                weights
+                if f == "-"
+                else export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1]
+            )  # weights
+            assert suffix in str(w), "export failed"
+            y.append([name, True])
+        except Exception:
+            y.append([name, False])  # mAP, t_inference
+
+    # Print results
+    LOGGER.info("\n")
+    parse_opt()
+    notebook_init()  # print system info
+    py = pd.DataFrame(y, columns=["Format", "Export"])
+    LOGGER.info(f"\nExports complete ({time.time() - t:.2f}s)")
+    LOGGER.info(str(py))
+    return py
+
+
+def parse_opt():
+    """
+    Parses command-line arguments for YOLOv5 model inference configuration.
+
+    Args:
+        weights (str): The path to the weights file. Defaults to 'ROOT / "yolov5s.pt"'.
+        imgsz (int): Inference size in pixels. Defaults to 640.
+        batch_size (int): Batch size. Defaults to 1.
+        data (str): Path to the dataset YAML file. Defaults to 'ROOT / "data/coco128.yaml"'.
+        device (str): CUDA device, e.g., '0' or '0,1,2,3' or 'cpu'. Defaults to an empty string (auto-select).
+        half (bool): Use FP16 half-precision inference. This is a flag and defaults to False.
+        test (bool): Test exports only. This is a flag and defaults to False.
+        pt_only (bool): Test PyTorch only. This is a flag and defaults to False.
+        hard_fail (bool | str): Throw an error on benchmark failure. Can be a boolean or a string representing a minimum
+            metric floor, e.g., '0.29'. Defaults to False.
+
+    Returns:
+        argparse.Namespace: Parsed command-line arguments encapsulated in an argparse Namespace object.
+
+    Notes:
+        The function modifies the 'opt.data' by checking and validating the YAML path using 'check_yaml()'.
+        The parsed arguments are printed for reference using 'print_args()'.
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path")
+    parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
+    parser.add_argument("--batch-size", type=int, default=1, help="batch size")
+    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
+    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+    parser.add_argument("--test", action="store_true", help="test exports only")
+    parser.add_argument("--pt-only", action="store_true", help="test PyTorch only")
+    parser.add_argument("--hard-fail", nargs="?", const=True, default=False, help="Exception on error or < min metric")
+    opt = parser.parse_args()
+    opt.data = check_yaml(opt.data)  # check YAML
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    """
+    Executes YOLOv5 benchmark tests or main training/inference routines based on the provided command-line arguments.
+
+    Args:
+        opt (argparse.Namespace): Parsed command-line arguments including options for weights, image size, batch size, data
+            configuration, device, and other flags for inference settings.
+
+    Returns:
+        None: This function does not return any value. It leverages side-effects such as logging and running benchmarks.
+
+    Example:
+        ```python
+        if __name__ == "__main__":
+            opt = parse_opt()
+            main(opt)
+        ```
+
+    Notes:
+        - For a complete list of supported export formats and their respective requirements, refer to the
+          [Ultralytics YOLOv5 Export Formats](https://github.com/ultralytics/yolov5#export-formats).
+        - Ensure that you have installed all necessary dependencies by following the installation instructions detailed in
+          the [main repository](https://github.com/ultralytics/yolov5#installation).
+
+        ```shell
+        # Running benchmarks on default weights and image size
+        $ python benchmarks.py --weights yolov5s.pt --img 640
+        ```
+    """
+    test(**vars(opt)) if opt.test else run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

yolov5/classify/predict.py

@@ -0,0 +1,241 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
+
+Usage - sources:
+    $ python classify/predict.py --weights yolov5s-cls.pt --source 0                               # webcam
+                                                                   img.jpg                         # image
+                                                                   vid.mp4                         # video
+                                                                   screen                          # screenshot
+                                                                   path/                           # directory
+                                                                   list.txt                        # list of images
+                                                                   list.streams                    # list of streams
+                                                                   'path/*.jpg'                    # glob
+                                                                   'https://youtu.be/LNwODJXcvt4'  # YouTube
+                                                                   'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+
+Usage - formats:
+    $ python classify/predict.py --weights yolov5s-cls.pt                 # PyTorch
+                                           yolov5s-cls.torchscript        # TorchScript
+                                           yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                           yolov5s-cls_openvino_model     # OpenVINO
+                                           yolov5s-cls.engine             # TensorRT
+                                           yolov5s-cls.mlmodel            # CoreML (macOS-only)
+                                           yolov5s-cls_saved_model        # TensorFlow SavedModel
+                                           yolov5s-cls.pb                 # TensorFlow GraphDef
+                                           yolov5s-cls.tflite             # TensorFlow Lite
+                                           yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
+                                           yolov5s-cls_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import os
+import platform
+import sys
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from ultralytics.utils.plotting import Annotator
+
+from models.common import DetectMultiBackend
+from utils.augmentations import classify_transforms
+from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
+from utils.general import (
+    LOGGER,
+    Profile,
+    check_file,
+    check_img_size,
+    check_imshow,
+    check_requirements,
+    colorstr,
+    cv2,
+    increment_path,
+    print_args,
+    strip_optimizer,
+)
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+    weights=ROOT / "yolov5s-cls.pt",  # model.pt path(s)
+    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    imgsz=(224, 224),  # inference size (height, width)
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    view_img=False,  # show results
+    save_txt=False,  # save results to *.txt
+    nosave=False,  # do not save images/videos
+    augment=False,  # augmented inference
+    visualize=False,  # visualize features
+    update=False,  # update all models
+    project=ROOT / "runs/predict-cls",  # save results to project/name
+    name="exp",  # save results to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    half=False,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    vid_stride=1,  # video frame-rate stride
+):
+    """Conducts YOLOv5 classification inference on diverse input sources and saves results."""
+    source = str(source)
+    save_img = not nosave and not source.endswith(".txt")  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
+    webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
+    screenshot = source.lower().startswith("screen")
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+    (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    bs = 1  # batch_size
+    if webcam:
+        view_img = check_imshow(warn=True)
+        dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
+        bs = len(dataset)
+    elif screenshot:
+        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
+    else:
+        dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
+    vid_path, vid_writer = [None] * bs, [None] * bs
+
+    # Run inference
+    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
+    for path, im, im0s, vid_cap, s in dataset:
+        with dt[0]:
+            im = torch.Tensor(im).to(model.device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+
+        # Inference
+        with dt[1]:
+            results = model(im)
+
+        # Post-process
+        with dt[2]:
+            pred = F.softmax(results, dim=1)  # probabilities
+
+        # Process predictions
+        for i, prob in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f"{i}: "
+            else:
+                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+            txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt
+
+            s += "{:g}x{:g} ".format(*im.shape[2:])  # print string
+            annotator = Annotator(im0, example=str(names), pil=True)
+
+            # Print results
+            top5i = prob.argsort(0, descending=True)[:5].tolist()  # top 5 indices
+            s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
+
+            # Write results
+            text = "\n".join(f"{prob[j]:.2f} {names[j]}" for j in top5i)
+            if save_img or view_img:  # Add bbox to image
+                annotator.text([32, 32], text, txt_color=(255, 255, 255))
+            if save_txt:  # Write to file
+                with open(f"{txt_path}.txt", "a") as f:
+                    f.write(text + "\n")
+
+            # Stream results
+            im0 = annotator.result()
+            if view_img:
+                if platform.system() == "Linux" and p not in windows:
+                    windows.append(p)
+                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
+                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == "image":
+                    cv2.imwrite(save_path, im0)
+                else:  # 'video' or 'stream'
+                    if vid_path[i] != save_path:  # new video
+                        vid_path[i] = save_path
+                        if isinstance(vid_writer[i], cv2.VideoWriter):
+                            vid_writer[i].release()  # release previous video writer
+                        if vid_cap:  # video
+                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                        else:  # stream
+                            fps, w, h = 30, im0.shape[1], im0.shape[0]
+                        save_path = str(Path(save_path).with_suffix(".mp4"))  # force *.mp4 suffix on results videos
+                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+                    vid_writer[i].write(im0)
+
+        # Print time (inference-only)
+        LOGGER.info(f"{s}{dt[1].dt * 1e3:.1f}ms")
+
+    # Print results
+    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
+    LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
+    if save_txt or save_img:
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    """Parses command line arguments for YOLOv5 inference settings including model, source, device, and image size."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model path(s)")
+    parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
+    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")
+    parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[224], help="inference size h,w")
+    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+    parser.add_argument("--view-img", action="store_true", help="show results")
+    parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
+    parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
+    parser.add_argument("--augment", action="store_true", help="augmented inference")
+    parser.add_argument("--visualize", action="store_true", help="visualize features")
+    parser.add_argument("--update", action="store_true", help="update all models")
+    parser.add_argument("--project", default=ROOT / "runs/predict-cls", help="save results to project/name")
+    parser.add_argument("--name", default="exp", help="save results to project/name")
+    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
+    parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    """Executes YOLOv5 model inference with options for ONNX DNN and video frame-rate stride adjustments."""
+    check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

yolov5/classify/train.py

@@ -0,0 +1,382 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+Train a YOLOv5 classifier model on a classification dataset.
+
+Usage - Single-GPU training:
+    $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224
+
+Usage - Multi-GPU DDP training:
+    $ python -m torch.distributed.run --nproc_per_node 4 --master_port 2022 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+
+Datasets:           --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
+YOLOv5-cls models:  --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
+Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+import time
+from copy import deepcopy
+from datetime import datetime
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+import torch.hub as hub
+import torch.optim.lr_scheduler as lr_scheduler
+import torchvision
+from torch.cuda import amp
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from classify import val as validate
+from models.experimental import attempt_load
+from models.yolo import ClassificationModel, DetectionModel
+from utils.dataloaders import create_classification_dataloader
+from utils.general import (
+    DATASETS_DIR,
+    LOGGER,
+    TQDM_BAR_FORMAT,
+    WorkingDirectory,
+    check_git_info,
+    check_git_status,
+    check_requirements,
+    colorstr,
+    download,
+    increment_path,
+    init_seeds,
+    print_args,
+    yaml_save,
+)
+from utils.loggers import GenericLogger
+from utils.plots import imshow_cls
+from utils.torch_utils import (
+    ModelEMA,
+    de_parallel,
+    model_info,
+    reshape_classifier_output,
+    select_device,
+    smart_DDP,
+    smart_optimizer,
+    smartCrossEntropyLoss,
+    torch_distributed_zero_first,
+)
+
+LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1))  # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv("RANK", -1))
+WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
+GIT_INFO = check_git_info()
+
+
+def train(opt, device):
+    """Trains a YOLOv5 model, managing datasets, model optimization, logging, and saving checkpoints."""
+    init_seeds(opt.seed + 1 + RANK, deterministic=True)
+    save_dir, data, bs, epochs, nw, imgsz, pretrained = (
+        opt.save_dir,
+        Path(opt.data),
+        opt.batch_size,
+        opt.epochs,
+        min(os.cpu_count() - 1, opt.workers),
+        opt.imgsz,
+        str(opt.pretrained).lower() == "true",
+    )
+    cuda = device.type != "cpu"
+
+    # Directories
+    wdir = save_dir / "weights"
+    wdir.mkdir(parents=True, exist_ok=True)  # make dir
+    last, best = wdir / "last.pt", wdir / "best.pt"
+
+    # Save run settings
+    yaml_save(save_dir / "opt.yaml", vars(opt))
+
+    # Logger
+    logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None
+
+    # Download Dataset
+    with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
+        data_dir = data if data.is_dir() else (DATASETS_DIR / data)
+        if not data_dir.is_dir():
+            LOGGER.info(f"\nDataset not found ⚠️, missing path {data_dir}, attempting download...")
+            t = time.time()
+            if str(data) == "imagenet":
+                subprocess.run(["bash", str(ROOT / "data/scripts/get_imagenet.sh")], shell=True, check=True)
+            else:
+                url = f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{data}.zip"
+                download(url, dir=data_dir.parent)
+            s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
+            LOGGER.info(s)
+
+    # Dataloaders
+    nc = len([x for x in (data_dir / "train").glob("*") if x.is_dir()])  # number of classes
+    trainloader = create_classification_dataloader(
+        path=data_dir / "train",
+        imgsz=imgsz,
+        batch_size=bs // WORLD_SIZE,
+        augment=True,
+        cache=opt.cache,
+        rank=LOCAL_RANK,
+        workers=nw,
+    )
+
+    test_dir = data_dir / "test" if (data_dir / "test").exists() else data_dir / "val"  # data/test or data/val
+    if RANK in {-1, 0}:
+        testloader = create_classification_dataloader(
+            path=test_dir,
+            imgsz=imgsz,
+            batch_size=bs // WORLD_SIZE * 2,
+            augment=False,
+            cache=opt.cache,
+            rank=-1,
+            workers=nw,
+        )
+
+    # Model
+    with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
+        if Path(opt.model).is_file() or opt.model.endswith(".pt"):
+            model = attempt_load(opt.model, device="cpu", fuse=False)
+        elif opt.model in torchvision.models.__dict__:  # TorchVision models i.e. resnet50, efficientnet_b0
+            model = torchvision.models.__dict__[opt.model](weights="IMAGENET1K_V1" if pretrained else None)
+        else:
+            m = hub.list("ultralytics/yolov5")  # + hub.list('pytorch/vision')  # models
+            raise ModuleNotFoundError(f"--model {opt.model} not found. Available models are: \n" + "\n".join(m))
+        if isinstance(model, DetectionModel):
+            LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
+            model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10)  # convert to classification model
+        reshape_classifier_output(model, nc)  # update class count
+    for m in model.modules():
+        if not pretrained and hasattr(m, "reset_parameters"):
+            m.reset_parameters()
+        if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
+            m.p = opt.dropout  # set dropout
+    for p in model.parameters():
+        p.requires_grad = True  # for training
+    model = model.to(device)
+
+    # Info
+    if RANK in {-1, 0}:
+        model.names = trainloader.dataset.classes  # attach class names
+        model.transforms = testloader.dataset.torch_transforms  # attach inference transforms
+        model_info(model)
+        if opt.verbose:
+            LOGGER.info(model)
+        images, labels = next(iter(trainloader))
+        file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / "train_images.jpg")
+        logger.log_images(file, name="Train Examples")
+        logger.log_graph(model, imgsz)  # log model
+
+    # Optimizer
+    optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay)
+
+    # Scheduler
+    lrf = 0.01  # final lr (fraction of lr0)
+
+    # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf  # cosine
+    def lf(x):
+        """Linear learning rate scheduler function, scaling learning rate from initial value to `lrf` over `epochs`."""
+        return (1 - x / epochs) * (1 - lrf) + lrf  # linear
+
+    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
+    # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,
+    #                                    final_div_factor=1 / 25 / lrf)
+
+    # EMA
+    ema = ModelEMA(model) if RANK in {-1, 0} else None
+
+    # DDP mode
+    if cuda and RANK != -1:
+        model = smart_DDP(model)
+
+    # Train
+    t0 = time.time()
+    criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing)  # loss function
+    best_fitness = 0.0
+    scaler = amp.GradScaler(enabled=cuda)
+    val = test_dir.stem  # 'val' or 'test'
+    LOGGER.info(
+        f"Image sizes {imgsz} train, {imgsz} test\n"
+        f"Using {nw * WORLD_SIZE} dataloader workers\n"
+        f"Logging results to {colorstr('bold', save_dir)}\n"
+        f"Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n"
+        f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}"
+    )
+    for epoch in range(epochs):  # loop over the dataset multiple times
+        tloss, vloss, fitness = 0.0, 0.0, 0.0  # train loss, val loss, fitness
+        model.train()
+        if RANK != -1:
+            trainloader.sampler.set_epoch(epoch)
+        pbar = enumerate(trainloader)
+        if RANK in {-1, 0}:
+            pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT)
+        for i, (images, labels) in pbar:  # progress bar
+            images, labels = images.to(device, non_blocking=True), labels.to(device)
+
+            # Forward
+            with amp.autocast(enabled=cuda):  # stability issues when enabled
+                loss = criterion(model(images), labels)
+
+            # Backward
+            scaler.scale(loss).backward()
+
+            # Optimize
+            scaler.unscale_(optimizer)  # unscale gradients
+            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0)  # clip gradients
+            scaler.step(optimizer)
+            scaler.update()
+            optimizer.zero_grad()
+            if ema:
+                ema.update(model)
+
+            if RANK in {-1, 0}:
+                # Print
+                tloss = (tloss * i + loss.item()) / (i + 1)  # update mean losses
+                mem = "%.3gG" % (torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0)  # (GB)
+                pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + " " * 36
+
+                # Test
+                if i == len(pbar) - 1:  # last batch
+                    top1, top5, vloss = validate.run(
+                        model=ema.ema, dataloader=testloader, criterion=criterion, pbar=pbar
+                    )  # test accuracy, loss
+                    fitness = top1  # define fitness as top1 accuracy
+
+        # Scheduler
+        scheduler.step()
+
+        # Log metrics
+        if RANK in {-1, 0}:
+            # Best fitness
+            if fitness > best_fitness:
+                best_fitness = fitness
+
+            # Log
+            metrics = {
+                "train/loss": tloss,
+                f"{val}/loss": vloss,
+                "metrics/accuracy_top1": top1,
+                "metrics/accuracy_top5": top5,
+                "lr/0": optimizer.param_groups[0]["lr"],
+            }  # learning rate
+            logger.log_metrics(metrics, epoch)
+
+            # Save model
+            final_epoch = epoch + 1 == epochs
+            if (not opt.nosave) or final_epoch:
+                ckpt = {
+                    "epoch": epoch,
+                    "best_fitness": best_fitness,
+                    "model": deepcopy(ema.ema).half(),  # deepcopy(de_parallel(model)).half(),
+                    "ema": None,  # deepcopy(ema.ema).half(),
+                    "updates": ema.updates,
+                    "optimizer": None,  # optimizer.state_dict(),
+                    "opt": vars(opt),
+                    "git": GIT_INFO,  # {remote, branch, commit} if a git repo
+                    "date": datetime.now().isoformat(),
+                }
+
+                # Save last, best and delete
+                torch.save(ckpt, last)
+                if best_fitness == fitness:
+                    torch.save(ckpt, best)
+                del ckpt
+
+    # Train complete
+    if RANK in {-1, 0} and final_epoch:
+        LOGGER.info(
+            f"\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)"
+            f"\nResults saved to {colorstr('bold', save_dir)}"
+            f"\nPredict:         python classify/predict.py --weights {best} --source im.jpg"
+            f"\nValidate:        python classify/val.py --weights {best} --data {data_dir}"
+            f"\nExport:          python export.py --weights {best} --include onnx"
+            f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
+            f"\nVisualize:       https://netron.app\n"
+        )
+
+        # Plot examples
+        images, labels = (x[:25] for x in next(iter(testloader)))  # first 25 images and labels
+        pred = torch.max(ema.ema(images.to(device)), 1)[1]
+        file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / "test_images.jpg")
+
+        # Log results
+        meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
+        logger.log_images(file, name="Test Examples (true-predicted)", epoch=epoch)
+        logger.log_model(best, epochs, metadata=meta)
+
+
+def parse_opt(known=False):
+    """Parses command line arguments for YOLOv5 training including model path, dataset, epochs, and more, returning
+    parsed arguments.
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", type=str, default="yolov5s-cls.pt", help="initial weights path")
+    parser.add_argument("--data", type=str, default="imagenette160", help="cifar10, cifar100, mnist, imagenet, ...")
+    parser.add_argument("--epochs", type=int, default=10, help="total training epochs")
+    parser.add_argument("--batch-size", type=int, default=64, help="total batch size for all GPUs")
+    parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="train, val image size (pixels)")
+    parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
+    parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"')
+    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+    parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
+    parser.add_argument("--project", default=ROOT / "runs/train-cls", help="save to project/name")
+    parser.add_argument("--name", default="exp", help="save to project/name")
+    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+    parser.add_argument("--pretrained", nargs="?", const=True, default=True, help="start from i.e. --pretrained False")
+    parser.add_argument("--optimizer", choices=["SGD", "Adam", "AdamW", "RMSProp"], default="Adam", help="optimizer")
+    parser.add_argument("--lr0", type=float, default=0.001, help="initial learning rate")
+    parser.add_argument("--decay", type=float, default=5e-5, help="weight decay")
+    parser.add_argument("--label-smoothing", type=float, default=0.1, help="Label smoothing epsilon")
+    parser.add_argument("--cutoff", type=int, default=None, help="Model layer cutoff index for Classify() head")
+    parser.add_argument("--dropout", type=float, default=None, help="Dropout (fraction)")
+    parser.add_argument("--verbose", action="store_true", help="Verbose mode")
+    parser.add_argument("--seed", type=int, default=0, help="Global training seed")
+    parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify")
+    return parser.parse_known_args()[0] if known else parser.parse_args()
+
+
+def main(opt):
+    """Executes YOLOv5 training with given options, handling device setup and DDP mode; includes pre-training checks."""
+    if RANK in {-1, 0}:
+        print_args(vars(opt))
+        check_git_status()
+        check_requirements(ROOT / "requirements.txt")
+
+    # DDP mode
+    device = select_device(opt.device, batch_size=opt.batch_size)
+    if LOCAL_RANK != -1:
+        assert opt.batch_size != -1, "AutoBatch is coming soon for classification, please pass a valid --batch-size"
+        assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE"
+        assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command"
+        torch.cuda.set_device(LOCAL_RANK)
+        device = torch.device("cuda", LOCAL_RANK)
+        dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
+
+    # Parameters
+    opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)  # increment run
+
+    # Train
+    train(opt, device)
+
+
+def run(**kwargs):
+    """
+    Executes YOLOv5 model training or inference with specified parameters, returning updated options.
+
+    Example: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
+    """
+    opt = parse_opt(True)
+    for k, v in kwargs.items():
+        setattr(opt, k, v)
+    main(opt)
+    return opt
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

yolov5/classify/val.py

@@ -0,0 +1,178 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+Validate a trained YOLOv5 classification model on a classification dataset.
+
+Usage:
+    $ bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+    $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate ImageNet
+
+Usage - formats:
+    $ python classify/val.py --weights yolov5s-cls.pt                 # PyTorch
+                                       yolov5s-cls.torchscript        # TorchScript
+                                       yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                       yolov5s-cls_openvino_model     # OpenVINO
+                                       yolov5s-cls.engine             # TensorRT
+                                       yolov5s-cls.mlmodel            # CoreML (macOS-only)
+                                       yolov5s-cls_saved_model        # TensorFlow SavedModel
+                                       yolov5s-cls.pb                 # TensorFlow GraphDef
+                                       yolov5s-cls.tflite             # TensorFlow Lite
+                                       yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
+                                       yolov5s-cls_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import os
+import sys
+from pathlib import Path
+
+import torch
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import create_classification_dataloader
+from utils.general import (
+    LOGGER,
+    TQDM_BAR_FORMAT,
+    Profile,
+    check_img_size,
+    check_requirements,
+    colorstr,
+    increment_path,
+    print_args,
+)
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+    data=ROOT / "../datasets/mnist",  # dataset dir
+    weights=ROOT / "yolov5s-cls.pt",  # model.pt path(s)
+    batch_size=128,  # batch size
+    imgsz=224,  # inference size (pixels)
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    workers=8,  # max dataloader workers (per RANK in DDP mode)
+    verbose=False,  # verbose output
+    project=ROOT / "runs/val-cls",  # save to project/name
+    name="exp",  # save to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    half=False,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    model=None,
+    dataloader=None,
+    criterion=None,
+    pbar=None,
+):
+    """Validates a YOLOv5 classification model on a dataset, computing metrics like top1 and top5 accuracy."""
+    # Initialize/load model and set device
+    training = model is not None
+    if training:  # called by train.py
+        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model
+        half &= device.type != "cpu"  # half precision only supported on CUDA
+        model.half() if half else model.float()
+    else:  # called directly
+        device = select_device(device, batch_size=batch_size)
+
+        # Directories
+        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+        save_dir.mkdir(parents=True, exist_ok=True)  # make dir
+
+        # Load model
+        model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
+        stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
+        imgsz = check_img_size(imgsz, s=stride)  # check image size
+        half = model.fp16  # FP16 supported on limited backends with CUDA
+        if engine:
+            batch_size = model.batch_size
+        else:
+            device = model.device
+            if not (pt or jit):
+                batch_size = 1  # export.py models default to batch-size 1
+                LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
+
+        # Dataloader
+        data = Path(data)
+        test_dir = data / "test" if (data / "test").exists() else data / "val"  # data/test or data/val
+        dataloader = create_classification_dataloader(
+            path=test_dir, imgsz=imgsz, batch_size=batch_size, augment=False, rank=-1, workers=workers
+        )
+
+    model.eval()
+    pred, targets, loss, dt = [], [], 0, (Profile(device=device), Profile(device=device), Profile(device=device))
+    n = len(dataloader)  # number of batches
+    action = "validating" if dataloader.dataset.root.stem == "val" else "testing"
+    desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
+    bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
+    with torch.cuda.amp.autocast(enabled=device.type != "cpu"):
+        for images, labels in bar:
+            with dt[0]:
+                images, labels = images.to(device, non_blocking=True), labels.to(device)
+
+            with dt[1]:
+                y = model(images)
+
+            with dt[2]:
+                pred.append(y.argsort(1, descending=True)[:, :5])
+                targets.append(labels)
+                if criterion:
+                    loss += criterion(y, labels)
+
+    loss /= n
+    pred, targets = torch.cat(pred), torch.cat(targets)
+    correct = (targets[:, None] == pred).float()
+    acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1)  # (top1, top5) accuracy
+    top1, top5 = acc.mean(0).tolist()
+
+    if pbar:
+        pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
+    if verbose:  # all classes
+        LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
+        LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
+        for i, c in model.names.items():
+            acc_i = acc[targets == i]
+            top1i, top5i = acc_i.mean(0).tolist()
+            LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
+
+        # Print results
+        t = tuple(x.t / len(dataloader.dataset.samples) * 1e3 for x in dt)  # speeds per image
+        shape = (1, 3, imgsz, imgsz)
+        LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}" % t)
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
+
+    return top1, top5, loss
+
+
+def parse_opt():
+    """Parses and returns command line arguments for YOLOv5 model evaluation and inference settings."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data", type=str, default=ROOT / "../datasets/mnist", help="dataset path")
+    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model.pt path(s)")
+    parser.add_argument("--batch-size", type=int, default=128, help="batch size")
+    parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="inference size (pixels)")
+    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+    parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
+    parser.add_argument("--verbose", nargs="?", const=True, default=True, help="verbose output")
+    parser.add_argument("--project", default=ROOT / "runs/val-cls", help="save to project/name")
+    parser.add_argument("--name", default="exp", help="save to project/name")
+    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
+    opt = parser.parse_args()
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    """Executes the YOLOv5 model prediction workflow, handling argument parsing and requirement checks."""
+    check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

yolov5/data/Argoverse.yaml

@@ -0,0 +1,73 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
+# Example usage: python train.py --data Argoverse.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── Argoverse  ← downloads here (31.3 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Argoverse # dataset root dir
+train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: bus
+  5: truck
+  6: traffic_light
+  7: stop_sign
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+
+  from tqdm import tqdm
+  from utils.general import download, Path
+
+
+  def argoverse2yolo(set):
+      labels = {}
+      a = json.load(open(set, "rb"))
+      for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
+          img_id = annot['image_id']
+          img_name = a['images'][img_id]['name']
+          img_label_name = f'{img_name[:-3]}txt'
+
+          cls = annot['category_id']  # instance class id
+          x_center, y_center, width, height = annot['bbox']
+          x_center = (x_center + width / 2) / 1920.0  # offset and scale
+          y_center = (y_center + height / 2) / 1200.0  # offset and scale
+          width /= 1920.0  # scale
+          height /= 1200.0  # scale
+
+          img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
+          if not img_dir.exists():
+              img_dir.mkdir(parents=True, exist_ok=True)
+
+          k = str(img_dir / img_label_name)
+          if k not in labels:
+              labels[k] = []
+          labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
+
+      for k in labels:
+          with open(k, "w") as f:
+              f.writelines(labels[k])
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
+  download(urls, dir=dir, delete=False)
+
+  # Convert
+  annotations_dir = 'Argoverse-HD/annotations/'
+  (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images')  # rename 'tracking' to 'images'
+  for d in "train.json", "val.json":
+      argoverse2yolo(dir / annotations_dir / d)  # convert VisDrone annotations to YOLO labels

yolov5/data/GlobalWheat2020.yaml

@@ -0,0 +1,53 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
+# Example usage: python train.py --data GlobalWheat2020.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── GlobalWheat2020  ← downloads here (7.0 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/GlobalWheat2020 # dataset root dir
+train: # train images (relative to 'path') 3422 images
+  - images/arvalis_1
+  - images/arvalis_2
+  - images/arvalis_3
+  - images/ethz_1
+  - images/rres_1
+  - images/inrae_1
+  - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+  - images/ethz_1
+test: # test images (optional) 1276 images
+  - images/utokyo_1
+  - images/utokyo_2
+  - images/nau_1
+  - images/uq_1
+
+# Classes
+names:
+  0: wheat_head
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from utils.general import download, Path
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
+          'https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip']
+  download(urls, dir=dir)
+
+  # Make Directories
+  for p in 'annotations', 'images', 'labels':
+      (dir / p).mkdir(parents=True, exist_ok=True)
+
+  # Move
+  for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
+           'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
+      (dir / p).rename(dir / 'images' / p)  # move to /images
+      f = (dir / p).with_suffix('.json')  # json file
+      if f.exists():
+          f.rename((dir / 'annotations' / p).with_suffix('.json'))  # move to /annotations

yolov5/data/ImageNet10.yaml

@@ -0,0 +1,31 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
+# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Example usage: python classify/train.py --data imagenet
+# parent
+# ├── yolov5
+# └── datasets
+#     └── imagenet10  ← downloads here
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/imagenet10 # dataset root dir
+train: train # train images (relative to 'path') 1281167 images
+val: val # val images (relative to 'path') 50000 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: tench
+  1: goldfish
+  2: great white shark
+  3: tiger shark
+  4: hammerhead shark
+  5: electric ray
+  6: stingray
+  7: cock
+  8: hen
+  9: ostrich
+
+# Download script/URL (optional)
+download: data/scripts/get_imagenet10.sh

yolov5/data/ImageNet100.yaml

@@ -0,0 +1,120 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
+# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Example usage: python classify/train.py --data imagenet
+# parent
+# ├── yolov5
+# └── datasets
+#     └── imagenet100  ← downloads here
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/imagenet100 # dataset root dir
+train: train # train images (relative to 'path') 1281167 images
+val: val # val images (relative to 'path') 50000 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: tench
+  1: goldfish
+  2: great white shark
+  3: tiger shark
+  4: hammerhead shark
+  5: electric ray
+  6: stingray
+  7: cock
+  8: hen
+  9: ostrich
+  10: brambling
+  11: goldfinch
+  12: house finch
+  13: junco
+  14: indigo bunting
+  15: American robin
+  16: bulbul
+  17: jay
+  18: magpie
+  19: chickadee
+  20: American dipper
+  21: kite
+  22: bald eagle
+  23: vulture
+  24: great grey owl
+  25: fire salamander
+  26: smooth newt
+  27: newt
+  28: spotted salamander
+  29: axolotl
+  30: American bullfrog
+  31: tree frog
+  32: tailed frog
+  33: loggerhead sea turtle
+  34: leatherback sea turtle
+  35: mud turtle
+  36: terrapin
+  37: box turtle
+  38: banded gecko
+  39: green iguana
+  40: Carolina anole
+  41: desert grassland whiptail lizard
+  42: agama
+  43: frilled-necked lizard
+  44: alligator lizard
+  45: Gila monster
+  46: European green lizard
+  47: chameleon
+  48: Komodo dragon
+  49: Nile crocodile
+  50: American alligator
+  51: triceratops
+  52: worm snake
+  53: ring-necked snake
+  54: eastern hog-nosed snake
+  55: smooth green snake
+  56: kingsnake
+  57: garter snake
+  58: water snake
+  59: vine snake
+  60: night snake
+  61: boa constrictor
+  62: African rock python
+  63: Indian cobra
+  64: green mamba
+  65: sea snake
+  66: Saharan horned viper
+  67: eastern diamondback rattlesnake
+  68: sidewinder
+  69: trilobite
+  70: harvestman
+  71: scorpion
+  72: yellow garden spider
+  73: barn spider
+  74: European garden spider
+  75: southern black widow
+  76: tarantula
+  77: wolf spider
+  78: tick
+  79: centipede
+  80: black grouse
+  81: ptarmigan
+  82: ruffed grouse
+  83: prairie grouse
+  84: peacock
+  85: quail
+  86: partridge
+  87: grey parrot
+  88: macaw
+  89: sulphur-crested cockatoo
+  90: lorikeet
+  91: coucal
+  92: bee eater
+  93: hornbill
+  94: hummingbird
+  95: jacamar
+  96: toucan
+  97: duck
+  98: red-breasted merganser
+  99: goose
+# Download script/URL (optional)
+download: data/scripts/get_imagenet100.sh

yolov5/data/Objects365.yaml

@@ -0,0 +1,437 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Objects365 dataset https://www.objects365.org/ by Megvii
+# Example usage: python train.py --data Objects365.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── Objects365  ← downloads here (712 GB = 367G data + 345G zips)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Objects365 # dataset root dir
+train: images/train # train images (relative to 'path') 1742289 images
+val: images/val # val images (relative to 'path') 80000 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: Person
+  1: Sneakers
+  2: Chair
+  3: Other Shoes
+  4: Hat
+  5: Car
+  6: Lamp
+  7: Glasses
+  8: Bottle
+  9: Desk
+  10: Cup
+  11: Street Lights
+  12: Cabinet/shelf
+  13: Handbag/Satchel
+  14: Bracelet
+  15: Plate
+  16: Picture/Frame
+  17: Helmet
+  18: Book
+  19: Gloves
+  20: Storage box
+  21: Boat
+  22: Leather Shoes
+  23: Flower
+  24: Bench
+  25: Potted Plant
+  26: Bowl/Basin
+  27: Flag
+  28: Pillow
+  29: Boots
+  30: Vase
+  31: Microphone
+  32: Necklace
+  33: Ring
+  34: SUV
+  35: Wine Glass
+  36: Belt
+  37: Monitor/TV
+  38: Backpack
+  39: Umbrella
+  40: Traffic Light
+  41: Speaker
+  42: Watch
+  43: Tie
+  44: Trash bin Can
+  45: Slippers
+  46: Bicycle
+  47: Stool
+  48: Barrel/bucket
+  49: Van
+  50: Couch
+  51: Sandals
+  52: Basket
+  53: Drum
+  54: Pen/Pencil
+  55: Bus
+  56: Wild Bird
+  57: High Heels
+  58: Motorcycle
+  59: Guitar
+  60: Carpet
+  61: Cell Phone
+  62: Bread
+  63: Camera
+  64: Canned
+  65: Truck
+  66: Traffic cone
+  67: Cymbal
+  68: Lifesaver
+  69: Towel
+  70: Stuffed Toy
+  71: Candle
+  72: Sailboat
+  73: Laptop
+  74: Awning
+  75: Bed
+  76: Faucet
+  77: Tent
+  78: Horse
+  79: Mirror
+  80: Power outlet
+  81: Sink
+  82: Apple
+  83: Air Conditioner
+  84: Knife
+  85: Hockey Stick
+  86: Paddle
+  87: Pickup Truck
+  88: Fork
+  89: Traffic Sign
+  90: Balloon
+  91: Tripod
+  92: Dog
+  93: Spoon
+  94: Clock
+  95: Pot
+  96: Cow
+  97: Cake
+  98: Dinning Table
+  99: Sheep
+  100: Hanger
+  101: Blackboard/Whiteboard
+  102: Napkin
+  103: Other Fish
+  104: Orange/Tangerine
+  105: Toiletry
+  106: Keyboard
+  107: Tomato
+  108: Lantern
+  109: Machinery Vehicle
+  110: Fan
+  111: Green Vegetables
+  112: Banana
+  113: Baseball Glove
+  114: Airplane
+  115: Mouse
+  116: Train
+  117: Pumpkin
+  118: Soccer
+  119: Skiboard
+  120: Luggage
+  121: Nightstand
+  122: Tea pot
+  123: Telephone
+  124: Trolley
+  125: Head Phone
+  126: Sports Car
+  127: Stop Sign
+  128: Dessert
+  129: Scooter
+  130: Stroller
+  131: Crane
+  132: Remote
+  133: Refrigerator
+  134: Oven
+  135: Lemon
+  136: Duck
+  137: Baseball Bat
+  138: Surveillance Camera
+  139: Cat
+  140: Jug
+  141: Broccoli
+  142: Piano
+  143: Pizza
+  144: Elephant
+  145: Skateboard
+  146: Surfboard
+  147: Gun
+  148: Skating and Skiing shoes
+  149: Gas stove
+  150: Donut
+  151: Bow Tie
+  152: Carrot
+  153: Toilet
+  154: Kite
+  155: Strawberry
+  156: Other Balls
+  157: Shovel
+  158: Pepper
+  159: Computer Box
+  160: Toilet Paper
+  161: Cleaning Products
+  162: Chopsticks
+  163: Microwave
+  164: Pigeon
+  165: Baseball
+  166: Cutting/chopping Board
+  167: Coffee Table
+  168: Side Table
+  169: Scissors
+  170: Marker
+  171: Pie
+  172: Ladder
+  173: Snowboard
+  174: Cookies
+  175: Radiator
+  176: Fire Hydrant
+  177: Basketball
+  178: Zebra
+  179: Grape
+  180: Giraffe
+  181: Potato
+  182: Sausage
+  183: Tricycle
+  184: Violin
+  185: Egg
+  186: Fire Extinguisher
+  187: Candy
+  188: Fire Truck
+  189: Billiards
+  190: Converter
+  191: Bathtub
+  192: Wheelchair
+  193: Golf Club
+  194: Briefcase
+  195: Cucumber
+  196: Cigar/Cigarette
+  197: Paint Brush
+  198: Pear
+  199: Heavy Truck
+  200: Hamburger
+  201: Extractor
+  202: Extension Cord
+  203: Tong
+  204: Tennis Racket
+  205: Folder
+  206: American Football
+  207: earphone
+  208: Mask
+  209: Kettle
+  210: Tennis
+  211: Ship
+  212: Swing
+  213: Coffee Machine
+  214: Slide
+  215: Carriage
+  216: Onion
+  217: Green beans
+  218: Projector
+  219: Frisbee
+  220: Washing Machine/Drying Machine
+  221: Chicken
+  222: Printer
+  223: Watermelon
+  224: Saxophone
+  225: Tissue
+  226: Toothbrush
+  227: Ice cream
+  228: Hot-air balloon
+  229: Cello
+  230: French Fries
+  231: Scale
+  232: Trophy
+  233: Cabbage
+  234: Hot dog
+  235: Blender
+  236: Peach
+  237: Rice
+  238: Wallet/Purse
+  239: Volleyball
+  240: Deer
+  241: Goose
+  242: Tape
+  243: Tablet
+  244: Cosmetics
+  245: Trumpet
+  246: Pineapple
+  247: Golf Ball
+  248: Ambulance
+  249: Parking meter
+  250: Mango
+  251: Key
+  252: Hurdle
+  253: Fishing Rod
+  254: Medal
+  255: Flute
+  256: Brush
+  257: Penguin
+  258: Megaphone
+  259: Corn
+  260: Lettuce
+  261: Garlic
+  262: Swan
+  263: Helicopter
+  264: Green Onion
+  265: Sandwich
+  266: Nuts
+  267: Speed Limit Sign
+  268: Induction Cooker
+  269: Broom
+  270: Trombone
+  271: Plum
+  272: Rickshaw
+  273: Goldfish
+  274: Kiwi fruit
+  275: Router/modem
+  276: Poker Card
+  277: Toaster
+  278: Shrimp
+  279: Sushi
+  280: Cheese
+  281: Notepaper
+  282: Cherry
+  283: Pliers
+  284: CD
+  285: Pasta
+  286: Hammer
+  287: Cue
+  288: Avocado
+  289: Hamimelon
+  290: Flask
+  291: Mushroom
+  292: Screwdriver
+  293: Soap
+  294: Recorder
+  295: Bear
+  296: Eggplant
+  297: Board Eraser
+  298: Coconut
+  299: Tape Measure/Ruler
+  300: Pig
+  301: Showerhead
+  302: Globe
+  303: Chips
+  304: Steak
+  305: Crosswalk Sign
+  306: Stapler
+  307: Camel
+  308: Formula 1
+  309: Pomegranate
+  310: Dishwasher
+  311: Crab
+  312: Hoverboard
+  313: Meat ball
+  314: Rice Cooker
+  315: Tuba
+  316: Calculator
+  317: Papaya
+  318: Antelope
+  319: Parrot
+  320: Seal
+  321: Butterfly
+  322: Dumbbell
+  323: Donkey
+  324: Lion
+  325: Urinal
+  326: Dolphin
+  327: Electric Drill
+  328: Hair Dryer
+  329: Egg tart
+  330: Jellyfish
+  331: Treadmill
+  332: Lighter
+  333: Grapefruit
+  334: Game board
+  335: Mop
+  336: Radish
+  337: Baozi
+  338: Target
+  339: French
+  340: Spring Rolls
+  341: Monkey
+  342: Rabbit
+  343: Pencil Case
+  344: Yak
+  345: Red Cabbage
+  346: Binoculars
+  347: Asparagus
+  348: Barbell
+  349: Scallop
+  350: Noddles
+  351: Comb
+  352: Dumpling
+  353: Oyster
+  354: Table Tennis paddle
+  355: Cosmetics Brush/Eyeliner Pencil
+  356: Chainsaw
+  357: Eraser
+  358: Lobster
+  359: Durian
+  360: Okra
+  361: Lipstick
+  362: Cosmetics Mirror
+  363: Curling
+  364: Table Tennis
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from tqdm import tqdm
+
+  from utils.general import Path, check_requirements, download, np, xyxy2xywhn
+
+  check_requirements('pycocotools>=2.0')
+  from pycocotools.coco import COCO
+
+  # Make Directories
+  dir = Path(yaml['path'])  # dataset root dir
+  for p in 'images', 'labels':
+      (dir / p).mkdir(parents=True, exist_ok=True)
+      for q in 'train', 'val':
+          (dir / p / q).mkdir(parents=True, exist_ok=True)
+
+  # Train, Val Splits
+  for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
+      print(f"Processing {split} in {patches} patches ...")
+      images, labels = dir / 'images' / split, dir / 'labels' / split
+
+      # Download
+      url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
+      if split == 'train':
+          download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False)  # annotations json
+          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
+      elif split == 'val':
+          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False)  # annotations json
+          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
+          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
+
+      # Move
+      for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
+          f.rename(images / f.name)  # move to /images/{split}
+
+      # Labels
+      coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
+      names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
+      for cid, cat in enumerate(names):
+          catIds = coco.getCatIds(catNms=[cat])
+          imgIds = coco.getImgIds(catIds=catIds)
+          for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
+              width, height = im["width"], im["height"]
+              path = Path(im["file_name"])  # image filename
+              try:
+                  with open(labels / path.with_suffix('.txt').name, 'a') as file:
+                      annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=False)
+                      for a in coco.loadAnns(annIds):
+                          x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
+                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
+                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
+                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
+              except Exception as e:
+                  print(e)

yolov5/data/SKU-110K.yaml

@@ -0,0 +1,52 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
+# Example usage: python train.py --data SKU-110K.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── SKU-110K  ← downloads here (13.6 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/SKU-110K # dataset root dir
+train: train.txt # train images (relative to 'path')  8219 images
+val: val.txt # val images (relative to 'path')  588 images
+test: test.txt # test images (optional)  2936 images
+
+# Classes
+names:
+  0: object
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import shutil
+  from tqdm import tqdm
+  from utils.general import np, pd, Path, download, xyxy2xywh
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  parent = Path(dir.parent)  # download dir
+  urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
+  download(urls, dir=parent, delete=False)
+
+  # Rename directories
+  if dir.exists():
+      shutil.rmtree(dir)
+  (parent / 'SKU110K_fixed').rename(dir)  # rename dir
+  (dir / 'labels').mkdir(parents=True, exist_ok=True)  # create labels dir
+
+  # Convert labels
+  names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height'  # column names
+  for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
+      x = pd.read_csv(dir / 'annotations' / d, names=names).values  # annotations
+      images, unique_images = x[:, 0], np.unique(x[:, 0])
+      with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
+          f.writelines(f'./images/{s}\n' for s in unique_images)
+      for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
+          cls = 0  # single-class dataset
+          with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
+              for r in x[images == im]:
+                  w, h = r[6], r[7]  # image width, height
+                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance
+                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label

yolov5/data/VOC.yaml

@@ -0,0 +1,99 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
+# Example usage: python train.py --data VOC.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── VOC  ← downloads here (2.8 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VOC
+train: # train images (relative to 'path')  16551 images
+  - images/train2012
+  - images/train2007
+  - images/val2012
+  - images/val2007
+val: # val images (relative to 'path')  4952 images
+  - images/test2007
+test: # test images (optional)
+  - images/test2007
+
+# Classes
+names:
+  0: aeroplane
+  1: bicycle
+  2: bird
+  3: boat
+  4: bottle
+  5: bus
+  6: car
+  7: cat
+  8: chair
+  9: cow
+  10: diningtable
+  11: dog
+  12: horse
+  13: motorbike
+  14: person
+  15: pottedplant
+  16: sheep
+  17: sofa
+  18: train
+  19: tvmonitor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import xml.etree.ElementTree as ET
+
+  from tqdm import tqdm
+  from utils.general import download, Path
+
+
+  def convert_label(path, lb_path, year, image_id):
+      def convert_box(size, box):
+          dw, dh = 1. / size[0], 1. / size[1]
+          x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
+          return x * dw, y * dh, w * dw, h * dh
+
+      in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
+      out_file = open(lb_path, 'w')
+      tree = ET.parse(in_file)
+      root = tree.getroot()
+      size = root.find('size')
+      w = int(size.find('width').text)
+      h = int(size.find('height').text)
+
+      names = list(yaml['names'].values())  # names list
+      for obj in root.iter('object'):
+          cls = obj.find('name').text
+          if cls in names and int(obj.find('difficult').text) != 1:
+              xmlbox = obj.find('bndbox')
+              bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
+              cls_id = names.index(cls)  # class id
+              out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
+  urls = [f'{url}VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
+          f'{url}VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
+          f'{url}VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
+  download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
+
+  # Convert
+  path = dir / 'images/VOCdevkit'
+  for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
+      imgs_path = dir / 'images' / f'{image_set}{year}'
+      lbs_path = dir / 'labels' / f'{image_set}{year}'
+      imgs_path.mkdir(exist_ok=True, parents=True)
+      lbs_path.mkdir(exist_ok=True, parents=True)
+
+      with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
+          image_ids = f.read().strip().split()
+      for id in tqdm(image_ids, desc=f'{image_set}{year}'):
+          f = path / f'VOC{year}/JPEGImages/{id}.jpg'  # old img path
+          lb_path = (lbs_path / f.name).with_suffix('.txt')  # new label path
+          f.rename(imgs_path / f.name)  # move image
+          convert_label(path, lb_path, year, id)  # convert labels to YOLO format

yolov5/data/VisDrone.yaml

@@ -0,0 +1,69 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
+# Example usage: python train.py --data VisDrone.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── VisDrone  ← downloads here (2.3 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VisDrone # dataset root dir
+train: VisDrone2019-DET-train/images # train images (relative to 'path')  6471 images
+val: VisDrone2019-DET-val/images # val images (relative to 'path')  548 images
+test: VisDrone2019-DET-test-dev/images # test images (optional)  1610 images
+
+# Classes
+names:
+  0: pedestrian
+  1: people
+  2: bicycle
+  3: car
+  4: van
+  5: truck
+  6: tricycle
+  7: awning-tricycle
+  8: bus
+  9: motor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from utils.general import download, os, Path
+
+  def visdrone2yolo(dir):
+      from PIL import Image
+      from tqdm import tqdm
+
+      def convert_box(size, box):
+          # Convert VisDrone box to YOLO xywh box
+          dw = 1. / size[0]
+          dh = 1. / size[1]
+          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
+
+      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
+      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
+      for f in pbar:
+          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
+          lines = []
+          with open(f, 'r') as file:  # read annotation.txt
+              for row in [x.split(',') for x in file.read().strip().splitlines()]:
+                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
+                      continue
+                  cls = int(row[5]) - 1
+                  box = convert_box(img_size, tuple(map(int, row[:4])))
+                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
+                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
+                      fl.writelines(lines)  # write label.txt
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  urls = ['https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip',
+          'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip',
+          'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip',
+          'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip']
+  download(urls, dir=dir, curl=True, threads=4)
+
+  # Convert
+  for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
+      visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels

yolov5/data/coco.yaml

@@ -0,0 +1,115 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO 2017 dataset http://cocodataset.org by Microsoft
+# Example usage: python train.py --data coco.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco  ← downloads here (20.1 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco # dataset root dir
+train: train2017.txt # train images (relative to 'path') 118287 images
+val: val2017.txt # val images (relative to 'path') 5000 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: |
+  from utils.general import download, Path
+
+
+  # Download labels
+  segments = False  # segment or box labels
+  dir = Path(yaml['path'])  # dataset root dir
+  url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
+  urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')]  # labels
+  download(urls, dir=dir.parent)
+
+  # Download data
+  urls = ['http://images.cocodataset.org/zips/train2017.zip',  # 19G, 118k images
+          'http://images.cocodataset.org/zips/val2017.zip',  # 1G, 5k images
+          'http://images.cocodataset.org/zips/test2017.zip']  # 7G, 41k images (optional)
+  download(urls, dir=dir / 'images', threads=3)

yolov5/data/coco128-seg.yaml

@@ -0,0 +1,100 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco128-seg  ← downloads here (7 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128-seg # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip

yolov5/data/coco128.yaml

@@ -0,0 +1,100 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco128  ← downloads here (7 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128 # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip

yolov5/data/hyps/hyp.Objects365.yaml

@@ -0,0 +1,35 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Hyperparameters for Objects365 training
+# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.00258
+lrf: 0.17
+momentum: 0.779
+weight_decay: 0.00058
+warmup_epochs: 1.33
+warmup_momentum: 0.86
+warmup_bias_lr: 0.0711
+box: 0.0539
+cls: 0.299
+cls_pw: 0.825
+obj: 0.632
+obj_pw: 1.0
+iou_t: 0.2
+anchor_t: 3.44
+anchors: 3.2
+fl_gamma: 0.0
+hsv_h: 0.0188
+hsv_s: 0.704
+hsv_v: 0.36
+degrees: 0.0
+translate: 0.0902
+scale: 0.491
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0

yolov5/data/hyps/hyp.VOC.yaml

@@ -0,0 +1,41 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Hyperparameters for VOC training
+# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+# YOLOv5 Hyperparameter Evolution Results
+# Best generation: 467
+# Last generation: 996
+#    metrics/precision,       metrics/recall,      metrics/mAP_0.5, metrics/mAP_0.5:0.95,         val/box_loss,         val/obj_loss,         val/cls_loss
+#              0.87729,              0.85125,              0.91286,              0.72664,            0.0076739,            0.0042529,            0.0013865
+
+lr0: 0.00334
+lrf: 0.15135
+momentum: 0.74832
+weight_decay: 0.00025
+warmup_epochs: 3.3835
+warmup_momentum: 0.59462
+warmup_bias_lr: 0.18657
+box: 0.02
+cls: 0.21638
+cls_pw: 0.5
+obj: 0.51728
+obj_pw: 0.67198
+iou_t: 0.2
+anchor_t: 3.3744
+fl_gamma: 0.0
+hsv_h: 0.01041
+hsv_s: 0.54703
+hsv_v: 0.27739
+degrees: 0.0
+translate: 0.04591
+scale: 0.75544
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 0.85834
+mixup: 0.04266
+copy_paste: 0.0
+anchors: 3.412

yolov5/data/hyps/hyp.no-augmentation.yaml

@@ -0,0 +1,36 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Hyperparameters when using Albumentations frameworks
+# python train.py --hyp hyp.no-augmentation.yaml
+# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.3 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 0.7 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+# this parameters are all zero since we want to use albumentation framework
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0 # image HSV-Hue augmentation (fraction)
+hsv_s: 0 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0 # image translation (+/- fraction)
+scale: 0 # image scale (+/- gain)
+shear: 0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.0 # image flip left-right (probability)
+mosaic: 0.0 # image mosaic (probability)
+mixup: 0.0 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)

yolov5/data/hyps/hyp.scratch-high.yaml

@@ -0,0 +1,35 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Hyperparameters for high-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.3 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 0.7 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.9 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.1 # image mixup (probability)
+copy_paste: 0.1 # segment copy-paste (probability)

yolov5/data/hyps/hyp.scratch-low.yaml

@@ -0,0 +1,35 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Hyperparameters for low-augmentation COCO training from scratch
+# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.5 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 1.0 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.5 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.0 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)

yolov5/data/hyps/hyp.scratch-med.yaml

@@ -0,0 +1,35 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Hyperparameters for medium-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.3 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 0.7 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.9 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.1 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)

yolov5/data/scripts/download_weights.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Download latest models from https://github.com/ultralytics/yolov5/releases
+# Example usage: bash data/scripts/download_weights.sh
+# parent
+# └── yolov5
+#     ├── yolov5s.pt  ← downloads here
+#     ├── yolov5m.pt
+#     └── ...
+
+python - <<EOF
+from utils.downloads import attempt_download
+
+p5 = list('nsmlx')  # P5 models
+p6 = [f'{x}6' for x in p5]  # P6 models
+cls = [f'{x}-cls' for x in p5]  # classification models
+seg = [f'{x}-seg' for x in p5]  # classification models
+
+for x in p5 + p6 + cls + seg:
+    attempt_download(f'weights/yolov5{x}.pt')
+
+EOF

yolov5/data/scripts/get_coco.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Download COCO 2017 dataset http://cocodataset.org
+# Example usage: bash data/scripts/get_coco.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco  ← downloads here
+
+# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    --test) test=true ;;
+    --segments) segments=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+  test=false
+  segments=false
+fi
+
+# Download/unzip labels
+d='../datasets' # unzip directory
+url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+if [ "$segments" == "true" ]; then
+  f='coco2017labels-segments.zip' # 168 MB
+else
+  f='coco2017labels.zip' # 46 MB
+fi
+echo 'Downloading' $url$f ' ...'
+curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+
+# Download/unzip images
+d='../datasets/coco/images' # unzip directory
+url=http://images.cocodataset.org/zips/
+if [ "$train" == "true" ]; then
+  f='train2017.zip' # 19G, 118k images
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+if [ "$val" == "true" ]; then
+  f='val2017.zip' # 1G, 5k images
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+if [ "$test" == "true" ]; then
+  f='test2017.zip' # 7G, 41k images (optional)
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+wait # finish background tasks

yolov5/data/scripts/get_coco128.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
+# Example usage: bash data/scripts/get_coco128.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco128  ← downloads here
+
+# Download/unzip images and labels
+d='../datasets' # unzip directory
+url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+f='coco128.zip' # or 'coco128-segments.zip', 68 MB
+echo 'Downloading' $url$f ' ...'
+curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+
+wait # finish background tasks

yolov5/data/scripts/get_imagenet.sh

@@ -0,0 +1,51 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Download ILSVRC2012 ImageNet dataset https://image-net.org
+# Example usage: bash data/scripts/get_imagenet.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── imagenet  ← downloads here
+
+# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+fi
+
+# Make dir
+d='../datasets/imagenet' # unzip directory
+mkdir -p $d && cd $d
+
+# Download/unzip train
+if [ "$train" == "true" ]; then
+  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
+  mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
+  tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
+  find . -name "*.tar" | while read NAME; do
+    mkdir -p "${NAME%.tar}"
+    tar -xf "${NAME}" -C "${NAME%.tar}"
+    rm -f "${NAME}"
+  done
+  cd ..
+fi
+
+# Download/unzip val
+if [ "$val" == "true" ]; then
+  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
+  mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
+  wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
+fi
+
+# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
+# rm train/n04266014/n04266014_10835.JPEG
+
+# TFRecords (optional)
+# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt

yolov5/data/scripts/get_imagenet10.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Download ILSVRC2012 ImageNet dataset https://image-net.org
+# Example usage: bash data/scripts/get_imagenet.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── imagenet  ← downloads here
+
+# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+fi
+
+# Make dir
+d='../datasets/imagenet10' # unzip directory
+mkdir -p $d && cd $d
+
+# Download/unzip train
+wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet10.zip
+unzip imagenet10.zip && rm imagenet10.zip

yolov5/data/scripts/get_imagenet100.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Download ILSVRC2012 ImageNet dataset https://image-net.org
+# Example usage: bash data/scripts/get_imagenet.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── imagenet  ← downloads here
+
+# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+fi
+
+# Make dir
+d='../datasets/imagenet100' # unzip directory
+mkdir -p $d && cd $d
+
+# Download/unzip train
+wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet100.zip
+unzip imagenet100.zip && rm imagenet100.zip

yolov5/data/scripts/get_imagenet1000.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Download ILSVRC2012 ImageNet dataset https://image-net.org
+# Example usage: bash data/scripts/get_imagenet.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── imagenet  ← downloads here
+
+# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+fi
+
+# Make dir
+d='../datasets/imagenet1000' # unzip directory
+mkdir -p $d && cd $d
+
+# Download/unzip train
+wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet1000.zip
+unzip imagenet1000.zip && rm imagenet1000.zip

yolov5/data/xView.yaml

@@ -0,0 +1,152 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
+# --------  DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command!  --------
+# Example usage: python train.py --data xView.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── xView  ← downloads here (20.7 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/xView # dataset root dir
+train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
+val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
+
+# Classes
+names:
+  0: Fixed-wing Aircraft
+  1: Small Aircraft
+  2: Cargo Plane
+  3: Helicopter
+  4: Passenger Vehicle
+  5: Small Car
+  6: Bus
+  7: Pickup Truck
+  8: Utility Truck
+  9: Truck
+  10: Cargo Truck
+  11: Truck w/Box
+  12: Truck Tractor
+  13: Trailer
+  14: Truck w/Flatbed
+  15: Truck w/Liquid
+  16: Crane Truck
+  17: Railway Vehicle
+  18: Passenger Car
+  19: Cargo Car
+  20: Flat Car
+  21: Tank car
+  22: Locomotive
+  23: Maritime Vessel
+  24: Motorboat
+  25: Sailboat
+  26: Tugboat
+  27: Barge
+  28: Fishing Vessel
+  29: Ferry
+  30: Yacht
+  31: Container Ship
+  32: Oil Tanker
+  33: Engineering Vehicle
+  34: Tower crane
+  35: Container Crane
+  36: Reach Stacker
+  37: Straddle Carrier
+  38: Mobile Crane
+  39: Dump Truck
+  40: Haul Truck
+  41: Scraper/Tractor
+  42: Front loader/Bulldozer
+  43: Excavator
+  44: Cement Mixer
+  45: Ground Grader
+  46: Hut/Tent
+  47: Shed
+  48: Building
+  49: Aircraft Hangar
+  50: Damaged Building
+  51: Facility
+  52: Construction Site
+  53: Vehicle Lot
+  54: Helipad
+  55: Storage Tank
+  56: Shipping container lot
+  57: Shipping Container
+  58: Pylon
+  59: Tower
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+  import os
+  from pathlib import Path
+
+  import numpy as np
+  from PIL import Image
+  from tqdm import tqdm
+
+  from utils.dataloaders import autosplit
+  from utils.general import download, xyxy2xywhn
+
+
+  def convert_labels(fname=Path('xView/xView_train.geojson')):
+      # Convert xView geoJSON labels to YOLO format
+      path = fname.parent
+      with open(fname) as f:
+          print(f'Loading {fname}...')
+          data = json.load(f)
+
+      # Make dirs
+      labels = Path(path / 'labels' / 'train')
+      os.system(f'rm -rf {labels}')
+      labels.mkdir(parents=True, exist_ok=True)
+
+      # xView classes 11-94 to 0-59
+      xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
+                           12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
+                           29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
+                           47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
+
+      shapes = {}
+      for feature in tqdm(data['features'], desc=f'Converting {fname}'):
+          p = feature['properties']
+          if p['bounds_imcoords']:
+              id = p['image_id']
+              file = path / 'train_images' / id
+              if file.exists():  # 1395.tif missing
+                  try:
+                      box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
+                      assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
+                      cls = p['type_id']
+                      cls = xview_class2index[int(cls)]  # xView class to 0-60
+                      assert 59 >= cls >= 0, f'incorrect class index {cls}'
+
+                      # Write YOLO label
+                      if id not in shapes:
+                          shapes[id] = Image.open(file).size
+                      box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
+                      with open((labels / id).with_suffix('.txt'), 'a') as f:
+                          f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n")  # write label.txt
+                  except Exception as e:
+                      print(f'WARNING: skipping one label for {file}: {e}')
+
+
+  # Download manually from https://challenge.xviewdataset.org
+  dir = Path(yaml['path'])  # dataset root dir
+  # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip',  # train labels
+  #         'https://d307kc0mrhucc3.cloudfront.net/train_images.zip',  # 15G, 847 train images
+  #         'https://d307kc0mrhucc3.cloudfront.net/val_images.zip']  # 5G, 282 val images (no labels)
+  # download(urls, dir=dir, delete=False)
+
+  # Convert labels
+  convert_labels(dir / 'xView_train.geojson')
+
+  # Move images
+  images = Path(dir / 'images')
+  images.mkdir(parents=True, exist_ok=True)
+  Path(dir / 'train_images').rename(dir / 'images' / 'train')
+  Path(dir / 'val_images').rename(dir / 'images' / 'val')
+
+  # Split
+  autosplit(dir / 'images' / 'train')

yolov5/detect.py

@@ -0,0 +1,438 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
+
+Usage - sources:
+    $ python detect.py --weights yolov5s.pt --source 0                               # webcam
+                                                     img.jpg                         # image
+                                                     vid.mp4                         # video
+                                                     screen                          # screenshot
+                                                     path/                           # directory
+                                                     list.txt                        # list of images
+                                                     list.streams                    # list of streams
+                                                     'path/*.jpg'                    # glob
+                                                     'https://youtu.be/LNwODJXcvt4'  # YouTube
+                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+
+Usage - formats:
+    $ python detect.py --weights yolov5s.pt                 # PyTorch
+                                 yolov5s.torchscript        # TorchScript
+                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                 yolov5s_openvino_model     # OpenVINO
+                                 yolov5s.engine             # TensorRT
+                                 yolov5s.mlpackage          # CoreML (macOS-only)
+                                 yolov5s_saved_model        # TensorFlow SavedModel
+                                 yolov5s.pb                 # TensorFlow GraphDef
+                                 yolov5s.tflite             # TensorFlow Lite
+                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
+                                 yolov5s_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import csv
+import os
+import platform
+import sys
+from pathlib import Path
+
+import torch
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from ultralytics.utils.plotting import Annotator, colors, save_one_box
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
+from utils.general import (
+    LOGGER,
+    Profile,
+    check_file,
+    check_img_size,
+    check_imshow,
+    check_requirements,
+    colorstr,
+    cv2,
+    increment_path,
+    non_max_suppression,
+    print_args,
+    scale_boxes,
+    strip_optimizer,
+    xyxy2xywh,
+)
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+    weights=ROOT / "yolov5s.pt",  # model path or triton URL
+    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
+    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
+    imgsz=(640, 640),  # inference size (height, width)
+    conf_thres=0.25,  # confidence threshold
+    iou_thres=0.45,  # NMS IOU threshold
+    max_det=1000,  # maximum detections per image
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    view_img=False,  # show results
+    save_txt=False,  # save results to *.txt
+    save_format=0,  # save boxes coordinates in YOLO format or Pascal-VOC format (0 for YOLO and 1 for Pascal-VOC)
+    save_csv=False,  # save results in CSV format
+    save_conf=False,  # save confidences in --save-txt labels
+    save_crop=False,  # save cropped prediction boxes
+    nosave=False,  # do not save images/videos
+    classes=None,  # filter by class: --class 0, or --class 0 2 3
+    agnostic_nms=False,  # class-agnostic NMS
+    augment=False,  # augmented inference
+    visualize=False,  # visualize features
+    update=False,  # update all models
+    project=ROOT / "runs/detect",  # save results to project/name
+    name="exp",  # save results to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    line_thickness=3,  # bounding box thickness (pixels)
+    hide_labels=False,  # hide labels
+    hide_conf=False,  # hide confidences
+    half=False,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    vid_stride=1,  # video frame-rate stride
+):
+    """
+    Runs YOLOv5 detection inference on various sources like images, videos, directories, streams, etc.
+
+    Args:
+        weights (str | Path): Path to the model weights file or a Triton URL. Default is 'yolov5s.pt'.
+        source (str | Path): Input source, which can be a file, directory, URL, glob pattern, screen capture, or webcam
+            index. Default is 'data/images'.
+        data (str | Path): Path to the dataset YAML file. Default is 'data/coco128.yaml'.
+        imgsz (tuple[int, int]): Inference image size as a tuple (height, width). Default is (640, 640).
+        conf_thres (float): Confidence threshold for detections. Default is 0.25.
+        iou_thres (float): Intersection Over Union (IOU) threshold for non-max suppression. Default is 0.45.
+        max_det (int): Maximum number of detections per image. Default is 1000.
+        device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu'. Default is an empty string, which uses the
+            best available device.
+        view_img (bool): If True, display inference results using OpenCV. Default is False.
+        save_txt (bool): If True, save results in a text file. Default is False.
+        save_csv (bool): If True, save results in a CSV file. Default is False.
+        save_conf (bool): If True, include confidence scores in the saved results. Default is False.
+        save_crop (bool): If True, save cropped prediction boxes. Default is False.
+        nosave (bool): If True, do not save inference images or videos. Default is False.
+        classes (list[int]): List of class indices to filter detections by. Default is None.
+        agnostic_nms (bool): If True, perform class-agnostic non-max suppression. Default is False.
+        augment (bool): If True, use augmented inference. Default is False.
+        visualize (bool): If True, visualize feature maps. Default is False.
+        update (bool): If True, update all models' weights. Default is False.
+        project (str | Path): Directory to save results. Default is 'runs/detect'.
+        name (str): Name of the current experiment; used to create a subdirectory within 'project'. Default is 'exp'.
+        exist_ok (bool): If True, existing directories with the same name are reused instead of being incremented. Default is
+            False.
+        line_thickness (int): Thickness of bounding box lines in pixels. Default is 3.
+        hide_labels (bool): If True, do not display labels on bounding boxes. Default is False.
+        hide_conf (bool): If True, do not display confidence scores on bounding boxes. Default is False.
+        half (bool): If True, use FP16 half-precision inference. Default is False.
+        dnn (bool): If True, use OpenCV DNN backend for ONNX inference. Default is False.
+        vid_stride (int): Stride for processing video frames, to skip frames between processing. Default is 1.
+
+    Returns:
+        None
+
+    Examples:
+        ```python
+        from ultralytics import run
+
+        # Run inference on an image
+        run(source='data/images/example.jpg', weights='yolov5s.pt', device='0')
+
+        # Run inference on a video with specific confidence threshold
+        run(source='data/videos/example.mp4', weights='yolov5s.pt', conf_thres=0.4, device='0')
+        ```
+    """
+    source = str(source)
+    save_img = not nosave and not source.endswith(".txt")  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
+    webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
+    screenshot = source.lower().startswith("screen")
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+    (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    bs = 1  # batch_size
+    if webcam:
+        view_img = check_imshow(warn=True)
+        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
+        bs = len(dataset)
+    elif screenshot:
+        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
+    else:
+        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
+    vid_path, vid_writer = [None] * bs, [None] * bs
+
+    # Run inference
+    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
+    for path, im, im0s, vid_cap, s in dataset:
+        with dt[0]:
+            im = torch.from_numpy(im).to(model.device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            im /= 255  # 0 - 255 to 0.0 - 1.0
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+            if model.xml and im.shape[0] > 1:
+                ims = torch.chunk(im, im.shape[0], 0)
+
+        # Inference
+        with dt[1]:
+            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
+            if model.xml and im.shape[0] > 1:
+                pred = None
+                for image in ims:
+                    if pred is None:
+                        pred = model(image, augment=augment, visualize=visualize).unsqueeze(0)
+                    else:
+                        pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0)
+                pred = [pred, None]
+            else:
+                pred = model(im, augment=augment, visualize=visualize)
+        # NMS
+        with dt[2]:
+            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
+
+        # Second-stage classifier (optional)
+        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
+
+        # Define the path for the CSV file
+        csv_path = save_dir / "predictions.csv"
+
+        # Create or append to the CSV file
+        def write_to_csv(image_name, prediction, confidence):
+            """Writes prediction data for an image to a CSV file, appending if the file exists."""
+            data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence}
+            file_exists = os.path.isfile(csv_path)
+            with open(csv_path, mode="a", newline="") as f:
+                writer = csv.DictWriter(f, fieldnames=data.keys())
+                if not file_exists:
+                    writer.writeheader()
+                writer.writerow(data)
+
+        # Process predictions
+        for i, det in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f"{i}: "
+            else:
+                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+            txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt
+            s += "{:g}x{:g} ".format(*im.shape[2:])  # print string
+            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
+            imc = im0.copy() if save_crop else im0  # for save_crop
+            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
+            if len(det):
+                # Rescale boxes from img_size to im0 size
+                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
+
+                # Print results
+                for c in det[:, 5].unique():
+                    n = (det[:, 5] == c).sum()  # detections per class
+                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
+
+                # Write results
+                for *xyxy, conf, cls in reversed(det):
+                    c = int(cls)  # integer class
+                    label = names[c] if hide_conf else f"{names[c]}"
+                    confidence = float(conf)
+                    confidence_str = f"{confidence:.2f}"
+
+                    if save_csv:
+                        write_to_csv(p.name, label, confidence_str)
+
+                    if save_txt:  # Write to file
+                        if save_format == 0:
+                            coords = (
+                                (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
+                            )  # normalized xywh
+                        else:
+                            coords = (torch.tensor(xyxy).view(1, 4) / gn).view(-1).tolist()  # xyxy
+                        line = (cls, *coords, conf) if save_conf else (cls, *coords)  # label format
+                        with open(f"{txt_path}.txt", "a") as f:
+                            f.write(("%g " * len(line)).rstrip() % line + "\n")
+
+                    if save_img or save_crop or view_img:  # Add bbox to image
+                        c = int(cls)  # integer class
+                        label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")
+                        annotator.box_label(xyxy, label, color=colors(c, True))
+                    if save_crop:
+                        save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)
+
+            # Stream results
+            im0 = annotator.result()
+            if view_img:
+                if platform.system() == "Linux" and p not in windows:
+                    windows.append(p)
+                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
+                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == "image":
+                    cv2.imwrite(save_path, im0)
+                else:  # 'video' or 'stream'
+                    if vid_path[i] != save_path:  # new video
+                        vid_path[i] = save_path
+                        if isinstance(vid_writer[i], cv2.VideoWriter):
+                            vid_writer[i].release()  # release previous video writer
+                        if vid_cap:  # video
+                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                        else:  # stream
+                            fps, w, h = 30, im0.shape[1], im0.shape[0]
+                        save_path = str(Path(save_path).with_suffix(".mp4"))  # force *.mp4 suffix on results videos
+                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+                    vid_writer[i].write(im0)
+
+        # Print time (inference-only)
+        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms")
+
+    # Print results
+    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
+    LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
+    if save_txt or save_img:
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    """
+    Parse command-line arguments for YOLOv5 detection, allowing custom inference options and model configurations.
+
+    Args:
+        --weights (str | list[str], optional): Model path or Triton URL. Defaults to ROOT / 'yolov5s.pt'.
+        --source (str, optional): File/dir/URL/glob/screen/0(webcam). Defaults to ROOT / 'data/images'.
+        --data (str, optional): Dataset YAML path. Provides dataset configuration information.
+        --imgsz (list[int], optional): Inference size (height, width). Defaults to [640].
+        --conf-thres (float, optional): Confidence threshold. Defaults to 0.25.
+        --iou-thres (float, optional): NMS IoU threshold. Defaults to 0.45.
+        --max-det (int, optional): Maximum number of detections per image. Defaults to 1000.
+        --device (str, optional): CUDA device, i.e., '0' or '0,1,2,3' or 'cpu'. Defaults to "".
+        --view-img (bool, optional): Flag to display results. Defaults to False.
+        --save-txt (bool, optional): Flag to save results to *.txt files. Defaults to False.
+        --save-csv (bool, optional): Flag to save results in CSV format. Defaults to False.
+        --save-conf (bool, optional): Flag to save confidences in labels saved via --save-txt. Defaults to False.
+        --save-crop (bool, optional): Flag to save cropped prediction boxes. Defaults to False.
+        --nosave (bool, optional): Flag to prevent saving images/videos. Defaults to False.
+        --classes (list[int], optional): List of classes to filter results by, e.g., '--classes 0 2 3'. Defaults to None.
+        --agnostic-nms (bool, optional): Flag for class-agnostic NMS. Defaults to False.
+        --augment (bool, optional): Flag for augmented inference. Defaults to False.
+        --visualize (bool, optional): Flag for visualizing features. Defaults to False.
+        --update (bool, optional): Flag to update all models in the model directory. Defaults to False.
+        --project (str, optional): Directory to save results. Defaults to ROOT / 'runs/detect'.
+        --name (str, optional): Sub-directory name for saving results within --project. Defaults to 'exp'.
+        --exist-ok (bool, optional): Flag to allow overwriting if the project/name already exists. Defaults to False.
+        --line-thickness (int, optional): Thickness (in pixels) of bounding boxes. Defaults to 3.
+        --hide-labels (bool, optional): Flag to hide labels in the output. Defaults to False.
+        --hide-conf (bool, optional): Flag to hide confidences in the output. Defaults to False.
+        --half (bool, optional): Flag to use FP16 half-precision inference. Defaults to False.
+        --dnn (bool, optional): Flag to use OpenCV DNN for ONNX inference. Defaults to False.
+        --vid-stride (int, optional): Video frame-rate stride, determining the number of frames to skip in between
+            consecutive frames. Defaults to 1.
+
+    Returns:
+        argparse.Namespace: Parsed command-line arguments as an argparse.Namespace object.
+
+    Example:
+        ```python
+        from ultralytics import YOLOv5
+        args = YOLOv5.parse_opt()
+        ```
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL")
+    parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
+    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")
+    parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")
+    parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold")
+    parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold")
+    parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")
+    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+    parser.add_argument("--view-img", action="store_true", help="show results")
+    parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
+    parser.add_argument(
+        "--save-format",
+        type=int,
+        default=0,
+        help="whether to save boxes coordinates in YOLO format or Pascal-VOC format when save-txt is True, 0 for YOLO and 1 for Pascal-VOC",
+    )
+    parser.add_argument("--save-csv", action="store_true", help="save results in CSV format")
+    parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
+    parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes")
+    parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
+    parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3")
+    parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS")
+    parser.add_argument("--augment", action="store_true", help="augmented inference")
+    parser.add_argument("--visualize", action="store_true", help="visualize features")
+    parser.add_argument("--update", action="store_true", help="update all models")
+    parser.add_argument("--project", default=ROOT / "runs/detect", help="save results to project/name")
+    parser.add_argument("--name", default="exp", help="save results to project/name")
+    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+    parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)")
+    parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels")
+    parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences")
+    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
+    parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    """
+    Executes YOLOv5 model inference based on provided command-line arguments, validating dependencies before running.
+
+    Args:
+        opt (argparse.Namespace): Command-line arguments for YOLOv5 detection. See function `parse_opt` for details.
+
+    Returns:
+        None
+
+    Note:
+        This function performs essential pre-execution checks and initiates the YOLOv5 detection process based on user-specified
+        options. Refer to the usage guide and examples for more information about different sources and formats at:
+        https://github.com/ultralytics/ultralytics
+
+    Example usage:
+
+    ```python
+    if __name__ == "__main__":
+        opt = parse_opt()
+        main(opt)
+    ```
+    """
+    check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

yolov5/hubconf.py

@@ -0,0 +1,510 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5.
+
+Usage:
+    import torch
+    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # official model
+    model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s')  # from branch
+    model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')  # custom/local model
+    model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local')  # local repo
+"""
+
+import torch
+
+
+def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+    """
+    Creates or loads a YOLOv5 model, with options for pretrained weights and model customization.
+
+    Args:
+        name (str): Model name (e.g., 'yolov5s') or path to the model checkpoint (e.g., 'path/to/best.pt').
+        pretrained (bool, optional): If True, loads pretrained weights into the model. Defaults to True.
+        channels (int, optional): Number of input channels the model expects. Defaults to 3.
+        classes (int, optional): Number of classes the model is expected to detect. Defaults to 80.
+        autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper for various input formats. Defaults to True.
+        verbose (bool, optional): If True, prints detailed information during the model creation/loading process. Defaults to True.
+        device (str | torch.device | None, optional): Device to use for model parameters (e.g., 'cpu', 'cuda'). If None, selects
+            the best available device. Defaults to None.
+
+    Returns:
+        (DetectMultiBackend | AutoShape): The loaded YOLOv5 model, potentially wrapped with AutoShape if specified.
+
+    Examples:
+        ```python
+        import torch
+        from ultralytics import _create
+
+        # Load an official YOLOv5s model with pretrained weights
+        model = _create('yolov5s')
+
+        # Load a custom model from a local checkpoint
+        model = _create('path/to/custom_model.pt', pretrained=False)
+
+        # Load a model with specific input channels and classes
+        model = _create('yolov5s', channels=1, classes=10)
+        ```
+
+    Notes:
+        For more information on model loading and customization, visit the
+        [YOLOv5 PyTorch Hub Documentation](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading).
+    """
+    from pathlib import Path
+
+    from models.common import AutoShape, DetectMultiBackend
+    from models.experimental import attempt_load
+    from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
+    from utils.downloads import attempt_download
+    from utils.general import LOGGER, ROOT, check_requirements, intersect_dicts, logging
+    from utils.torch_utils import select_device
+
+    if not verbose:
+        LOGGER.setLevel(logging.WARNING)
+    check_requirements(ROOT / "requirements.txt", exclude=("opencv-python", "tensorboard", "thop"))
+    name = Path(name)
+    path = name.with_suffix(".pt") if name.suffix == "" and not name.is_dir() else name  # checkpoint path
+    try:
+        device = select_device(device)
+        if pretrained and channels == 3 and classes == 80:
+            try:
+                model = DetectMultiBackend(path, device=device, fuse=autoshape)  # detection model
+                if autoshape:
+                    if model.pt and isinstance(model.model, ClassificationModel):
+                        LOGGER.warning(
+                            "WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. "
+                            "You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)."
+                        )
+                    elif model.pt and isinstance(model.model, SegmentationModel):
+                        LOGGER.warning(
+                            "WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. "
+                            "You will not be able to run inference with this model."
+                        )
+                    else:
+                        model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
+            except Exception:
+                model = attempt_load(path, device=device, fuse=False)  # arbitrary model
+        else:
+            cfg = list((Path(__file__).parent / "models").rglob(f"{path.stem}.yaml"))[0]  # model.yaml path
+            model = DetectionModel(cfg, channels, classes)  # create model
+            if pretrained:
+                ckpt = torch.load(attempt_download(path), map_location=device)  # load
+                csd = ckpt["model"].float().state_dict()  # checkpoint state_dict as FP32
+                csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"])  # intersect
+                model.load_state_dict(csd, strict=False)  # load
+                if len(ckpt["model"].names) == classes:
+                    model.names = ckpt["model"].names  # set class names attribute
+        if not verbose:
+            LOGGER.setLevel(logging.INFO)  # reset to default
+        return model.to(device)
+
+    except Exception as e:
+        help_url = "https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading"
+        s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help."
+        raise Exception(s) from e
+
+
+def custom(path="path/to/model.pt", autoshape=True, _verbose=True, device=None):
+    """
+    Loads a custom or local YOLOv5 model from a given path with optional autoshaping and device specification.
+
+    Args:
+        path (str): Path to the custom model file (e.g., 'path/to/model.pt').
+        autoshape (bool): Apply YOLOv5 .autoshape() wrapper to model if True, enabling compatibility with various input
+            types (default is True).
+        _verbose (bool): If True, prints all informational messages to the screen; otherwise, operates silently
+            (default is True).
+        device (str | torch.device | None): Device to load the model on, e.g., 'cpu', 'cuda', torch.device('cuda:0'), etc.
+            (default is None, which automatically selects the best available device).
+
+    Returns:
+        torch.nn.Module: A YOLOv5 model loaded with the specified parameters.
+
+    Notes:
+        For more details on loading models from PyTorch Hub:
+        https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading
+
+    Examples:
+        ```python
+        # Load model from a given path with autoshape enabled on the best available device
+        model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')
+
+        # Load model from a local path without autoshape on the CPU device
+        model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local', autoshape=False, device='cpu')
+        ```
+    """
+    return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
+
+
+def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
+    """
+    Instantiates the YOLOv5-nano model with options for pretraining, input channels, class count, autoshaping,
+    verbosity, and device.
+
+    Args:
+        pretrained (bool): If True, loads pretrained weights into the model. Defaults to True.
+        channels (int): Number of input channels for the model. Defaults to 3.
+        classes (int): Number of classes for object detection. Defaults to 80.
+        autoshape (bool): If True, applies the YOLOv5 .autoshape() wrapper to the model for various formats (file/URI/PIL/
+            cv2/np) and non-maximum suppression (NMS) during inference. Defaults to True.
+        _verbose (bool): If True, prints detailed information to the screen. Defaults to True.
+        device (str | torch.device | None): Specifies the device to use for model computation. If None, uses the best device
+            available (i.e., GPU if available, otherwise CPU). Defaults to None.
+
+    Returns:
+        DetectionModel | ClassificationModel | SegmentationModel: The instantiated YOLOv5-nano model, potentially with
+            pretrained weights and autoshaping applied.
+
+    Notes:
+        For further details on loading models from PyTorch Hub, refer to [PyTorch Hub models](https://pytorch.org/hub/
+        ultralytics_yolov5).
+
+    Examples:
+        ```python
+        import torch
+        from ultralytics import yolov5n
+
+        # Load the YOLOv5-nano model with defaults
+        model = yolov5n()
+
+        # Load the YOLOv5-nano model with a specific device
+        model = yolov5n(device='cuda')
+        ```
+    """
+    return _create("yolov5n", pretrained, channels, classes, autoshape, _verbose, device)
+
+
+def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
+    """
+    Create a YOLOv5-small (yolov5s) model with options for pretraining, input channels, class count, autoshaping,
+    verbosity, and device configuration.
+
+    Args:
+        pretrained (bool, optional): Flag to load pretrained weights into the model. Defaults to True.
+        channels (int, optional): Number of input channels. Defaults to 3.
+        classes (int, optional): Number of model classes. Defaults to 80.
+        autoshape (bool, optional): Whether to wrap the model with YOLOv5's .autoshape() for handling various input formats.
+            Defaults to True.
+        _verbose (bool, optional): Flag to print detailed information regarding model loading. Defaults to True.
+        device (str | torch.device | None, optional): Device to use for model computation, can be 'cpu', 'cuda', or
+            torch.device instances. If None, automatically selects the best available device. Defaults to None.
+
+    Returns:
+        torch.nn.Module: The YOLOv5-small model configured and loaded according to the specified parameters.
+
+    Example:
+        ```python
+        import torch
+
+        # Load the official YOLOv5-small model with pretrained weights
+        model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+
+        # Load the YOLOv5-small model from a specific branch
+        model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s')
+
+        # Load a custom YOLOv5-small model from a local checkpoint
+        model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')
+
+        # Load a local YOLOv5-small model specifying source as local repository
+        model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local')
+        ```
+
+    Notes:
+        For more details on model loading and customization, visit
+        the [YOLOv5 PyTorch Hub Documentation](https://pytorch.org/hub/ultralytics_yolov5).
+    """
+    return _create("yolov5s", pretrained, channels, classes, autoshape, _verbose, device)
+
+
+def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
+    """
+    Instantiates the YOLOv5-medium model with customizable pretraining, channel count, class count, autoshaping,
+    verbosity, and device.
+
+    Args:
+        pretrained (bool, optional): Whether to load pretrained weights into the model. Default is True.
+        channels (int, optional): Number of input channels. Default is 3.
+        classes (int, optional): Number of model classes. Default is 80.
+        autoshape (bool, optional): Apply YOLOv5 .autoshape() wrapper to the model for handling various input formats.
+            Default is True.
+        _verbose (bool, optional): Whether to print detailed information to the screen. Default is True.
+        device (str | torch.device | None, optional): Device specification to use for model parameters (e.g., 'cpu', 'cuda').
+            Default is None.
+
+    Returns:
+        torch.nn.Module: The instantiated YOLOv5-medium model.
+
+    Usage Example:
+        ```python
+        import torch
+
+        model = torch.hub.load('ultralytics/yolov5', 'yolov5m')  # Load YOLOv5-medium from Ultralytics repository
+        model = torch.hub.load('ultralytics/yolov5:master', 'yolov5m')  # Load from the master branch
+        model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m.pt')  # Load a custom/local YOLOv5-medium model
+        model = torch.hub.load('.', 'custom', 'yolov5m.pt', source='local')  # Load from a local repository
+        ```
+
+    For more information, visit https://pytorch.org/hub/ultralytics_yolov5.
+    """
+    return _create("yolov5m", pretrained, channels, classes, autoshape, _verbose, device)
+
+
+def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
+    """
+    Creates YOLOv5-large model with options for pretraining, channels, classes, autoshaping, verbosity, and device
+    selection.
+
+    Args:
+        pretrained (bool): Load pretrained weights into the model. Default is True.
+        channels (int): Number of input channels. Default is 3.
+        classes (int): Number of model classes. Default is 80.
+        autoshape (bool): Apply YOLOv5 .autoshape() wrapper to model. Default is True.
+        _verbose (bool): Print all information to screen. Default is True.
+        device (str | torch.device | None): Device to use for model parameters, e.g., 'cpu', 'cuda', or a torch.device instance.
+            Default is None.
+
+    Returns:
+        YOLOv5 model (torch.nn.Module): The YOLOv5-large model instantiated with specified configurations and possibly
+        pretrained weights.
+
+    Examples:
+        ```python
+        import torch
+        model = torch.hub.load('ultralytics/yolov5', 'yolov5l')
+        ```
+
+    Notes:
+        For additional details, refer to the PyTorch Hub models documentation:
+        https://pytorch.org/hub/ultralytics_yolov5
+    """
+    return _create("yolov5l", pretrained, channels, classes, autoshape, _verbose, device)
+
+
+def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
+    """
+    Perform object detection using the YOLOv5-xlarge model with options for pretraining, input channels, class count,
+    autoshaping, verbosity, and device specification.
+
+    Args:
+        pretrained (bool): If True, loads pretrained weights into the model. Defaults to True.
+        channels (int): Number of input channels for the model. Defaults to 3.
+        classes (int): Number of model classes for object detection. Defaults to 80.
+        autoshape (bool): If True, applies the YOLOv5 .autoshape() wrapper for handling different input formats. Defaults to
+            True.
+        _verbose (bool): If True, prints detailed information during model loading. Defaults to True.
+        device (str | torch.device | None): Device specification for computing the model, e.g., 'cpu', 'cuda:0', torch.device('cuda').
+            Defaults to None.
+
+    Returns:
+        torch.nn.Module: The YOLOv5-xlarge model loaded with the specified parameters, optionally with pretrained weights and
+        autoshaping applied.
+
+    Example:
+        ```python
+        import torch
+        model = torch.hub.load('ultralytics/yolov5', 'yolov5x')
+        ```
+
+    For additional details, refer to the official YOLOv5 PyTorch Hub models documentation:
+    https://pytorch.org/hub/ultralytics_yolov5
+    """
+    return _create("yolov5x", pretrained, channels, classes, autoshape, _verbose, device)
+
+
+def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
+    """
+    Creates YOLOv5-nano-P6 model with options for pretraining, channels, classes, autoshaping, verbosity, and device.
+
+    Args:
+        pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True.
+        channels (int, optional): Number of input channels. Default is 3.
+        classes (int, optional): Number of model classes. Default is 80.
+        autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper to the model. Default is True.
+        _verbose (bool, optional): If True, prints all information to screen. Default is True.
+        device (str | torch.device | None, optional): Device to use for model parameters. Can be 'cpu', 'cuda', or None.
+            Default is None.
+
+    Returns:
+        torch.nn.Module: YOLOv5-nano-P6 model loaded with the specified configurations.
+
+    Example:
+        ```python
+        import torch
+        model = yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device='cuda')
+        ```
+
+    Notes:
+        For more information on PyTorch Hub models, visit: https://pytorch.org/hub/ultralytics_yolov5
+    """
+    return _create("yolov5n6", pretrained, channels, classes, autoshape, _verbose, device)
+
+
+def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
+    """
+    Instantiate the YOLOv5-small-P6 model with options for pretraining, input channels, number of classes, autoshaping,
+    verbosity, and device selection.
+
+    Args:
+        pretrained (bool): If True, loads pretrained weights. Default is True.
+        channels (int): Number of input channels. Default is 3.
+        classes (int): Number of object detection classes. Default is 80.
+        autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model, allowing for varied input formats.
+            Default is True.
+        _verbose (bool): If True, prints detailed information during model loading. Default is True.
+        device (str | torch.device | None): Device specification for model parameters (e.g., 'cpu', 'cuda', or torch.device).
+            Default is None, which selects an available device automatically.
+
+    Returns:
+        torch.nn.Module: The YOLOv5-small-P6 model instance.
+
+    Usage:
+        ```python
+        import torch
+
+        model = torch.hub.load('ultralytics/yolov5', 'yolov5s6')
+        model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s6')  # load from a specific branch
+        model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/yolov5s6.pt')  # custom/local model
+        model = torch.hub.load('.', 'custom', 'path/to/yolov5s6.pt', source='local')  # local repo model
+        ```
+
+    Notes:
+        - For more information, refer to the PyTorch Hub models documentation at https://pytorch.org/hub/ultralytics_yolov5
+
+    Raises:
+        Exception: If there is an error during model creation or loading, with a suggestion to visit the YOLOv5
+            tutorials for help.
+    """
+    return _create("yolov5s6", pretrained, channels, classes, autoshape, _verbose, device)
+
+
+def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
+    """
+    Create YOLOv5-medium-P6 model with options for pretraining, channel count, class count, autoshaping, verbosity, and
+    device.
+
+    Args:
+        pretrained (bool): If True, loads pretrained weights. Default is True.
+        channels (int): Number of input channels. Default is 3.
+        classes (int): Number of model classes. Default is 80.
+        autoshape (bool): Apply YOLOv5 .autoshape() wrapper to the model for file/URI/PIL/cv2/np inputs and NMS.
+            Default is True.
+        _verbose (bool): If True, prints detailed information to the screen. Default is True.
+        device (str | torch.device | None): Device to use for model parameters. Default is None, which uses the
+            best available device.
+
+    Returns:
+        torch.nn.Module: The YOLOv5-medium-P6 model.
+
+    Refer to the PyTorch Hub models documentation: https://pytorch.org/hub/ultralytics_yolov5 for additional details.
+
+    Example:
+        ```python
+        import torch
+
+        # Load YOLOv5-medium-P6 model
+        model = torch.hub.load('ultralytics/yolov5', 'yolov5m6')
+        ```
+
+    Notes:
+        - The model can be loaded with pre-trained weights for better performance on specific tasks.
+        - The autoshape feature simplifies input handling by allowing various popular data formats.
+    """
+    return _create("yolov5m6", pretrained, channels, classes, autoshape, _verbose, device)
+
+
+def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
+    """
+    Instantiate the YOLOv5-large-P6 model with options for pretraining, channel and class counts, autoshaping,
+    verbosity, and device selection.
+
+    Args:
+        pretrained (bool, optional): If True, load pretrained weights into the model. Default is True.
+        channels (int, optional): Number of input channels. Default is 3.
+        classes (int, optional): Number of model classes. Default is 80.
+        autoshape (bool, optional): If True, apply YOLOv5 .autoshape() wrapper to the model for input flexibility. Default is True.
+        _verbose (bool, optional): If True, print all information to the screen. Default is True.
+        device (str | torch.device | None, optional): Device to use for model parameters, e.g., 'cpu', 'cuda', or torch.device.
+            If None, automatically selects the best available device. Default is None.
+
+    Returns:
+        torch.nn.Module: The instantiated YOLOv5-large-P6 model.
+
+    Example:
+        ```python
+        import torch
+        model = torch.hub.load('ultralytics/yolov5', 'yolov5l6')  # official model
+        model = torch.hub.load('ultralytics/yolov5:master', 'yolov5l6')  # from specific branch
+        model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/yolov5l6.pt')  # custom/local model
+        model = torch.hub.load('.', 'custom', 'path/to/yolov5l6.pt', source='local')  # local repository
+        ```
+
+    Note:
+        Refer to [PyTorch Hub Documentation](https://pytorch.org/hub/ultralytics_yolov5) for additional usage instructions.
+    """
+    return _create("yolov5l6", pretrained, channels, classes, autoshape, _verbose, device)
+
+
+def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
+    """
+    Creates the YOLOv5-xlarge-P6 model with options for pretraining, number of input channels, class count, autoshaping,
+    verbosity, and device selection.
+
+    Args:
+        pretrained (bool): If True, loads pretrained weights into the model. Default is True.
+        channels (int): Number of input channels. Default is 3.
+        classes (int): Number of model classes. Default is 80.
+        autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model. Default is True.
+        _verbose (bool): If True, prints all information to the screen. Default is True.
+        device (str | torch.device | None): Device to use for model parameters, can be a string, torch.device object, or
+            None for default device selection. Default is None.
+
+    Returns:
+        torch.nn.Module: The instantiated YOLOv5-xlarge-P6 model.
+
+    Example:
+        ```python
+        import torch
+        model = torch.hub.load('ultralytics/yolov5', 'yolov5x6')  # load the YOLOv5-xlarge-P6 model
+        ```
+
+    Note:
+        For more information on YOLOv5 models, visit the official documentation:
+        https://docs.ultralytics.com/yolov5
+    """
+    return _create("yolov5x6", pretrained, channels, classes, autoshape, _verbose, device)
+
+
+if __name__ == "__main__":
+    import argparse
+    from pathlib import Path
+
+    import numpy as np
+    from PIL import Image
+
+    from utils.general import cv2, print_args
+
+    # Argparser
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", type=str, default="yolov5s", help="model name")
+    opt = parser.parse_args()
+    print_args(vars(opt))
+
+    # Model
+    model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
+    # model = custom(path='path/to/model.pt')  # custom
+
+    # Images
+    imgs = [
+        "data/images/zidane.jpg",  # filename
+        Path("data/images/zidane.jpg"),  # Path
+        "https://ultralytics.com/images/zidane.jpg",  # URI
+        cv2.imread("data/images/bus.jpg")[:, :, ::-1],  # OpenCV
+        Image.open("data/images/bus.jpg"),  # PIL
+        np.zeros((320, 640, 3)),
+    ]  # numpy
+
+    # Inference
+    results = model(imgs, size=320)  # batched inference
+
+    # Results
+    results.print()
+    results.save()