主题功能完成
This commit is contained in:
24
.idea/inspectionProfiles/Project_Default.xml
generated
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24
.idea/inspectionProfiles/Project_Default.xml
generated
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@ -0,0 +1,24 @@
|
||||
<component name="InspectionProjectProfileManager">
|
||||
<profile version="1.0">
|
||||
<option name="myName" value="Project Default" />
|
||||
<inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
|
||||
<inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
|
||||
<option name="ignoredPackages">
|
||||
<value>
|
||||
<list size="3">
|
||||
<item index="0" class="java.lang.String" itemvalue="protobuf" />
|
||||
<item index="1" class="java.lang.String" itemvalue="mkl-random" />
|
||||
<item index="2" class="java.lang.String" itemvalue="numpy" />
|
||||
</list>
|
||||
</value>
|
||||
</option>
|
||||
</inspection_tool>
|
||||
<inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
|
||||
<option name="ignoredErrors">
|
||||
<list>
|
||||
<option value="N803" />
|
||||
</list>
|
||||
</option>
|
||||
</inspection_tool>
|
||||
</profile>
|
||||
</component>
|
||||
6
.idea/inspectionProfiles/profiles_settings.xml
generated
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6
.idea/inspectionProfiles/profiles_settings.xml
generated
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@ -0,0 +1,6 @@
|
||||
<component name="InspectionProjectProfileManager">
|
||||
<settings>
|
||||
<option name="USE_PROJECT_PROFILE" value="false" />
|
||||
<version value="1.0" />
|
||||
</settings>
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||||
</component>
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||||
@ -153,13 +153,19 @@ def get_img_leafer(image_id: int, session: Session = Depends(get_db)):
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@project.get("/run_train/{project_id}")
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def run_train(project_id: int, session: Session = Depends(get_db)):
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async def run_train(project_id: int, session: Session = Depends(get_db)):
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"""
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执行项目训练方法
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:param project_id:
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:param session:
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:return:
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"""
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project_info = pic.get_project_by_id(project_id, session)
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if project_info is None:
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return rc.response_error("项目查询错误")
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if project_info.project_status == '1':
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return rc.response_error("项目当前存在训练进程，请稍后再试")
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data, project, name, epochs, yolo_path, version_path = ps.run_train_yolo(project_info, session)
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data, project_name, name = ps.run_train_yolo(project_info, session)
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return StreamingResponse(
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ps.run_commend(data, project, name, epochs, yolo_path, version_path, project_id, session),
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ps.run_commend(data, project_name, name, 10, project_id, session),
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media_type="text/plain")
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@ -1,42 +0,0 @@
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import asyncio
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import subprocess
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from fastapi import APIRouter
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from fastapi.responses import StreamingResponse
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test = APIRouter()
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async def generate_data():
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for i in range(1, 10): # 生成 5 行数据
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await asyncio.sleep(1) # 等待 1 秒
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yield f"data: This is line {i}\n\n" # 返回 SSE 格式的数据
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def run_command(command):
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"""执行命令并实时打印每一行输出"""
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# 启动子进程
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with subprocess.Popen(
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command,
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shell=True,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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universal_newlines=True, # 确保输出以字符串形式返回而不是字节
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bufsize=1, # 行缓冲
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) as process:
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# 使用iter逐行读取stdout和stderr
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for line in process.stdout:
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yield f"stdout: {line.strip()} \n"
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for line in process.stderr:
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yield f"stderr: {line.strip()} \n"
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# 等待进程结束并获取返回码
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return_code = process.wait()
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if return_code != 0:
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print(f"Process exited with non-zero code: {return_code}")
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@test.get("/stream")
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async def stream_response():
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return StreamingResponse(run_command(["ping", "-n", "10", "127.0.0.1"]), media_type="text/plain")
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@ -9,7 +9,6 @@ from app.api.sys.login_api import login
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from app.api.sys.sys_user_api import user
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from app.api.business.project_api import project
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from app.api.common.view_img import view
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from app.api.common.test_api import test
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my_app = FastAPI()
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@ -36,5 +35,4 @@ my_app.include_router(upload_files, prefix="/upload", tags=["文件上传API"])
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my_app.include_router(view, prefix="/view_img", tags=["查看图片"])
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my_app.include_router(user, prefix="/user", tags=["用户管理API"])
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my_app.include_router(project, prefix="/proj", tags=["项目管理API"])
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my_app.include_router(test, prefix="/test", tags=["测试用API"])
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|
||||
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@ -14,6 +14,7 @@ from sqlalchemy.orm import Session
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from typing import List
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from fastapi import UploadFile
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import yaml
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import select
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import subprocess
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||||
|
||||
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@ -113,12 +114,6 @@ def run_train_yolo(project_info: ProjectInfoOut, session: Session):
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# 查询项目所属标签，返回两个 id，name一一对应的数组
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label_id_list, label_name_list = plc.get_label_for_train(project_info.id, session)
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# 在根目录创建classes.txt文件
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classes_txt = os.file_path(train_path, 'classes.txt')
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with open(classes_txt, 'w', encoding='utf-8') as file:
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for label_name in label_name_list:
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file.write(label_name + '\n')
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# 创建图片的的两个文件夹
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img_path_train = os.create_folder(train_path, 'images', 'train')
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img_path_val = os.create_folder(train_path, 'images', 'val')
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@ -152,35 +147,34 @@ def run_train_yolo(project_info: ProjectInfoOut, session: Session):
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data = yaml_file
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project = os.file_path(runs_url, project_info.project_no, 'train')
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name = version_path
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epochs = 10
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yolo_path = os.file_path(yolo_url, 'train.py')
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return data, project, name, epochs, yolo_path, version_path
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return data, project, name
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def run_commend(data: str, project: str,
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name: str, epochs: int,
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yolo_path: str, version_path: str,
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project_id: int, session: Session):
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yolo_path = os.file_path(yolo_url, 'train.py')
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yield f"stdout: 模型训练开始，请稍等。。。"
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# 启动子进程
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with subprocess.Popen(
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["python", yolo_path,
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["python", '-u', yolo_path,
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"--data=" + data,
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"--project=" + project,
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"--name=" + name,
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"--epochs=" + str(epochs)],
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shell=True,
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||||
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
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||||
shell=False,
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||||
stdout=subprocess.PIPE,
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||||
stderr=subprocess.PIPE,
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||||
universal_newlines=True, # 确保输出以字符串形式返回而不是字节
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||||
bufsize=1, # 行缓冲
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||||
stderr=subprocess.STDOUT, # 这里可以显示yolov5训练过程中出现的进度条等信息
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||||
text=True, # 缓存内容为文本，避免后续编码显示问题
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||||
encoding='utf-8',
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||||
) as process:
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||||
# 使用iter逐行读取stdout和stderr
|
||||
for line in process.stdout:
|
||||
yield f"stdout: {line.strip()} \n"
|
||||
|
||||
for line in process.stderr:
|
||||
yield f"stderr: {line.strip()} \n"
|
||||
while process.poll() is None:
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||||
line = process.stdout.readline()
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||||
process.stdout.flush() # 刷新缓存，防止缓存过多造成卡死
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||||
if line != '\n':
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||||
yield line
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||||
|
||||
# 等待进程结束并获取返回码
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||||
return_code = process.wait()
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||||
@ -191,7 +185,7 @@ def run_commend(data: str, project: str,
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# 然后保存版本训练信息
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train = ProjectTrain()
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train.project_id = project_id
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train.train_version = version_path
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||||
train.train_version = name
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bast_pt_path = os.file_path(project, name, 'weight', 'bast.pt')
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||||
last_pt_path = os.file_path(project, name, 'weight', 'last.pt')
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||||
train.best_pt = bast_pt_path
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||||
|
||||
222
yolov5/.dockerignore
Normal file
222
yolov5/.dockerignore
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@ -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
|
||||
2
yolov5/.gitattributes
vendored
Normal file
2
yolov5/.gitattributes
vendored
Normal file
@ -0,0 +1,2 @@
|
||||
# this drop notebooks from GitHub language stats
|
||||
*.ipynb linguist-vendored
|
||||
258
yolov5/.gitignore
vendored
Normal file
258
yolov5/.gitignore
vendored
Normal file
@ -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
|
||||
76
yolov5/CONTRIBUTING.md
Normal file
76
yolov5/CONTRIBUTING.md
Normal file
@ -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/)
|
||||
661
yolov5/LICENSE
Normal file
661
yolov5/LICENSE
Normal file
@ -0,0 +1,661 @@
|
||||
GNU AFFERO GENERAL PUBLIC LICENSE
|
||||
Version 3, 19 November 2007
|
||||
|
||||
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The GNU Affero General Public License is a free, copyleft license for
|
||||
software and other kinds of works, specifically designed to ensure
|
||||
cooperation with the community in the case of network server software.
|
||||
|
||||
The licenses for most software and other practical works are designed
|
||||
to take away your freedom to share and change the works. By contrast,
|
||||
our General Public Licenses are intended to guarantee your freedom to
|
||||
share and change all versions of a program--to make sure it remains free
|
||||
software for all its users.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
them if you wish), that you receive source code or can get it if you
|
||||
want it, that you can change the software or use pieces of it in new
|
||||
free programs, and that you know you can do these things.
|
||||
|
||||
Developers that use our General Public Licenses protect your rights
|
||||
with two steps: (1) assert copyright on the software, and (2) offer
|
||||
you this License which gives you legal permission to copy, distribute
|
||||
and/or modify the software.
|
||||
|
||||
A secondary benefit of defending all users' freedom is that
|
||||
improvements made in alternate versions of the program, if they
|
||||
receive widespread use, become available for other developers to
|
||||
incorporate. Many developers of free software are heartened and
|
||||
encouraged by the resulting cooperation. However, in the case of
|
||||
software used on network servers, this result may fail to come about.
|
||||
The GNU General Public License permits making a modified version and
|
||||
letting the public access it on a server without ever releasing its
|
||||
source code to the public.
|
||||
|
||||
The GNU Affero General Public License is designed specifically to
|
||||
ensure that, in such cases, the modified source code becomes available
|
||||
to the community. It requires the operator of a network server to
|
||||
provide the source code of the modified version running there to the
|
||||
users of that server. Therefore, public use of a modified version, on
|
||||
a publicly accessible server, gives the public access to the source
|
||||
code of the modified version.
|
||||
|
||||
An older license, called the Affero General Public License and
|
||||
published by Affero, was designed to accomplish similar goals. This is
|
||||
a different license, not a version of the Affero GPL, but Affero has
|
||||
released a new version of the Affero GPL which permits relicensing under
|
||||
this license.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
TERMS AND CONDITIONS
|
||||
|
||||
0. Definitions.
|
||||
|
||||
"This License" refers to version 3 of the GNU Affero General Public License.
|
||||
|
||||
"Copyright" also means copyright-like laws that apply to other kinds of
|
||||
works, such as semiconductor masks.
|
||||
|
||||
"The Program" refers to any copyrightable work licensed under this
|
||||
License. Each licensee is addressed as "you". "Licensees" and
|
||||
"recipients" may be individuals or organizations.
|
||||
|
||||
To "modify" a work means to copy from or adapt all or part of the work
|
||||
in a fashion requiring copyright permission, other than the making of an
|
||||
exact copy. The resulting work is called a "modified version" of the
|
||||
earlier work or a work "based on" the earlier work.
|
||||
|
||||
A "covered work" means either the unmodified Program or a work based
|
||||
on the Program.
|
||||
|
||||
To "propagate" a work means to do anything with it that, without
|
||||
permission, would make you directly or secondarily liable for
|
||||
infringement under applicable copyright law, except executing it on a
|
||||
computer or modifying a private copy. Propagation includes copying,
|
||||
distribution (with or without modification), making available to the
|
||||
public, and in some countries other activities as well.
|
||||
|
||||
To "convey" a work means any kind of propagation that enables other
|
||||
parties to make or receive copies. Mere interaction with a user through
|
||||
a computer network, with no transfer of a copy, is not conveying.
|
||||
|
||||
An interactive user interface displays "Appropriate Legal Notices"
|
||||
to the extent that it includes a convenient and prominently visible
|
||||
feature that (1) displays an appropriate copyright notice, and (2)
|
||||
tells the user that there is no warranty for the work (except to the
|
||||
extent that warranties are provided), that licensees may convey the
|
||||
work under this License, and how to view a copy of this License. If
|
||||
the interface presents a list of user commands or options, such as a
|
||||
menu, a prominent item in the list meets this criterion.
|
||||
|
||||
1. Source Code.
|
||||
|
||||
The "source code" for a work means the preferred form of the work
|
||||
for making modifications to it. "Object code" means any non-source
|
||||
form of a work.
|
||||
|
||||
A "Standard Interface" means an interface that either is an official
|
||||
standard defined by a recognized standards body, or, in the case of
|
||||
interfaces specified for a particular programming language, one that
|
||||
is widely used among developers working in that language.
|
||||
|
||||
The "System Libraries" of an executable work include anything, other
|
||||
than the work as a whole, that (a) is included in the normal form of
|
||||
packaging a Major Component, but which is not part of that Major
|
||||
Component, and (b) serves only to enable use of the work with that
|
||||
Major Component, or to implement a Standard Interface for which an
|
||||
implementation is available to the public in source code form. A
|
||||
"Major Component", in this context, means a major essential component
|
||||
(kernel, window system, and so on) of the specific operating system
|
||||
(if any) on which the executable work runs, or a compiler used to
|
||||
produce the work, or an object code interpreter used to run it.
|
||||
|
||||
The "Corresponding Source" for a work in object code form means all
|
||||
the source code needed to generate, install, and (for an executable
|
||||
work) run the object code and to modify the work, including scripts to
|
||||
control those activities. However, it does not include the work's
|
||||
System Libraries, or general-purpose tools or generally available free
|
||||
programs which are used unmodified in performing those activities but
|
||||
which are not part of the work. For example, Corresponding Source
|
||||
includes interface definition files associated with source files for
|
||||
the work, and the source code for shared libraries and dynamically
|
||||
linked subprograms that the work is specifically designed to require,
|
||||
such as by intimate data communication or control flow between those
|
||||
subprograms and other parts of the work.
|
||||
|
||||
The Corresponding Source need not include anything that users
|
||||
can regenerate automatically from other parts of the Corresponding
|
||||
Source.
|
||||
|
||||
The Corresponding Source for a work in source code form is that
|
||||
same work.
|
||||
|
||||
2. Basic Permissions.
|
||||
|
||||
All rights granted under this License are granted for the term of
|
||||
copyright on the Program, and are irrevocable provided the stated
|
||||
conditions are met. This License explicitly affirms your unlimited
|
||||
permission to run the unmodified Program. The output from running a
|
||||
covered work is covered by this License only if the output, given its
|
||||
content, constitutes a covered work. This License acknowledges your
|
||||
rights of fair use or other equivalent, as provided by copyright law.
|
||||
|
||||
You may make, run and propagate covered works that you do not
|
||||
convey, without conditions so long as your license otherwise remains
|
||||
in force. You may convey covered works to others for the sole purpose
|
||||
of having them make modifications exclusively for you, or provide you
|
||||
with facilities for running those works, provided that you comply with
|
||||
the terms of this License in conveying all material for which you do
|
||||
not control copyright. Those thus making or running the covered works
|
||||
for you must do so exclusively on your behalf, under your direction
|
||||
and control, on terms that prohibit them from making any copies of
|
||||
your copyrighted material outside their relationship with you.
|
||||
|
||||
Conveying under any other circumstances is permitted solely under
|
||||
the conditions stated below. Sublicensing is not allowed; section 10
|
||||
makes it unnecessary.
|
||||
|
||||
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
||||
|
||||
No covered work shall be deemed part of an effective technological
|
||||
measure under any applicable law fulfilling obligations under article
|
||||
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
||||
similar laws prohibiting or restricting circumvention of such
|
||||
measures.
|
||||
|
||||
When you convey a covered work, you waive any legal power to forbid
|
||||
circumvention of technological measures to the extent such circumvention
|
||||
is effected by exercising rights under this License with respect to
|
||||
the covered work, and you disclaim any intention to limit operation or
|
||||
modification of the work as a means of enforcing, against the work's
|
||||
users, your or third parties' legal rights to forbid circumvention of
|
||||
technological measures.
|
||||
|
||||
4. Conveying Verbatim Copies.
|
||||
|
||||
You may convey verbatim copies of the Program's source code as you
|
||||
receive it, in any medium, provided that you conspicuously and
|
||||
appropriately publish on each copy an appropriate copyright notice;
|
||||
keep intact all notices stating that this License and any
|
||||
non-permissive terms added in accord with section 7 apply to the code;
|
||||
keep intact all notices of the absence of any warranty; and give all
|
||||
recipients a copy of this License along with the Program.
|
||||
|
||||
You may charge any price or no price for each copy that you convey,
|
||||
and you may offer support or warranty protection for a fee.
|
||||
|
||||
5. Conveying Modified Source Versions.
|
||||
|
||||
You may convey a work based on the Program, or the modifications to
|
||||
produce it from the Program, in the form of source code under the
|
||||
terms of section 4, provided that you also meet all of these conditions:
|
||||
|
||||
a) The work must carry prominent notices stating that you modified
|
||||
it, and giving a relevant date.
|
||||
|
||||
b) The work must carry prominent notices stating that it is
|
||||
released under this License and any conditions added under section
|
||||
7. This requirement modifies the requirement in section 4 to
|
||||
"keep intact all notices".
|
||||
|
||||
c) You must license the entire work, as a whole, under this
|
||||
License to anyone who comes into possession of a copy. This
|
||||
License will therefore apply, along with any applicable section 7
|
||||
additional terms, to the whole of the work, and all its parts,
|
||||
regardless of how they are packaged. This License gives no
|
||||
permission to license the work in any other way, but it does not
|
||||
invalidate such permission if you have separately received it.
|
||||
|
||||
d) If the work has interactive user interfaces, each must display
|
||||
Appropriate Legal Notices; however, if the Program has interactive
|
||||
interfaces that do not display Appropriate Legal Notices, your
|
||||
work need not make them do so.
|
||||
|
||||
A compilation of a covered work with other separate and independent
|
||||
works, which are not by their nature extensions of the covered work,
|
||||
and which are not combined with it such as to form a larger program,
|
||||
in or on a volume of a storage or distribution medium, is called an
|
||||
"aggregate" if the compilation and its resulting copyright are not
|
||||
used to limit the access or legal rights of the compilation's users
|
||||
beyond what the individual works permit. Inclusion of a covered work
|
||||
in an aggregate does not cause this License to apply to the other
|
||||
parts of the aggregate.
|
||||
|
||||
6. Conveying Non-Source Forms.
|
||||
|
||||
You may convey a covered work in object code form under the terms
|
||||
of sections 4 and 5, provided that you also convey the
|
||||
machine-readable Corresponding Source under the terms of this License,
|
||||
in one of these ways:
|
||||
|
||||
a) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by the
|
||||
Corresponding Source fixed on a durable physical medium
|
||||
customarily used for software interchange.
|
||||
|
||||
b) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by a
|
||||
written offer, valid for at least three years and valid for as
|
||||
long as you offer spare parts or customer support for that product
|
||||
model, to give anyone who possesses the object code either (1) a
|
||||
copy of the Corresponding Source for all the software in the
|
||||
product that is covered by this License, on a durable physical
|
||||
medium customarily used for software interchange, for a price no
|
||||
more than your reasonable cost of physically performing this
|
||||
conveying of source, or (2) access to copy the
|
||||
Corresponding Source from a network server at no charge.
|
||||
|
||||
c) Convey individual copies of the object code with a copy of the
|
||||
written offer to provide the Corresponding Source. This
|
||||
alternative is allowed only occasionally and noncommercially, and
|
||||
only if you received the object code with such an offer, in accord
|
||||
with subsection 6b.
|
||||
|
||||
d) Convey the object code by offering access from a designated
|
||||
place (gratis or for a charge), and offer equivalent access to the
|
||||
Corresponding Source in the same way through the same place at no
|
||||
further charge. You need not require recipients to copy the
|
||||
Corresponding Source along with the object code. If the place to
|
||||
copy the object code is a network server, the Corresponding Source
|
||||
may be on a different server (operated by you or a third party)
|
||||
that supports equivalent copying facilities, provided you maintain
|
||||
clear directions next to the object code saying where to find the
|
||||
Corresponding Source. Regardless of what server hosts the
|
||||
Corresponding Source, you remain obligated to ensure that it is
|
||||
available for as long as needed to satisfy these requirements.
|
||||
|
||||
e) Convey the object code using peer-to-peer transmission, provided
|
||||
you inform other peers where the object code and Corresponding
|
||||
Source of the work are being offered to the general public at no
|
||||
charge under subsection 6d.
|
||||
|
||||
A separable portion of the object code, whose source code is excluded
|
||||
from the Corresponding Source as a System Library, need not be
|
||||
included in conveying the object code work.
|
||||
|
||||
A "User Product" is either (1) a "consumer product", which means any
|
||||
tangible personal property which is normally used for personal, family,
|
||||
or household purposes, or (2) anything designed or sold for incorporation
|
||||
into a dwelling. In determining whether a product is a consumer product,
|
||||
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||
product received by a particular user, "normally used" refers to a
|
||||
typical or common use of that class of product, regardless of the status
|
||||
of the particular user or of the way in which the particular user
|
||||
actually uses, or expects or is expected to use, the product. A product
|
||||
is a consumer product regardless of whether the product has substantial
|
||||
commercial, industrial or non-consumer uses, unless such uses represent
|
||||
the only significant mode of use of the product.
|
||||
|
||||
"Installation Information" for a User Product means any methods,
|
||||
procedures, authorization keys, or other information required to install
|
||||
and execute modified versions of a covered work in that User Product from
|
||||
a modified version of its Corresponding Source. The information must
|
||||
suffice to ensure that the continued functioning of the modified object
|
||||
code is in no case prevented or interfered with solely because
|
||||
modification has been made.
|
||||
|
||||
If you convey an object code work under this section in, or with, or
|
||||
specifically for use in, a User Product, and the conveying occurs as
|
||||
part of a transaction in which the right of possession and use of the
|
||||
User Product is transferred to the recipient in perpetuity or for a
|
||||
fixed term (regardless of how the transaction is characterized), the
|
||||
Corresponding Source conveyed under this section must be accompanied
|
||||
by the Installation Information. But this requirement does not apply
|
||||
if neither you nor any third party retains the ability to install
|
||||
modified object code on the User Product (for example, the work has
|
||||
been installed in ROM).
|
||||
|
||||
The requirement to provide Installation Information does not include a
|
||||
requirement to continue to provide support service, warranty, or updates
|
||||
for a work that has been modified or installed by the recipient, or for
|
||||
the User Product in which it has been modified or installed. Access to a
|
||||
network may be denied when the modification itself materially and
|
||||
adversely affects the operation of the network or violates the rules and
|
||||
protocols for communication across the network.
|
||||
|
||||
Corresponding Source conveyed, and Installation Information provided,
|
||||
in accord with this section must be in a format that is publicly
|
||||
documented (and with an implementation available to the public in
|
||||
source code form), and must require no special password or key for
|
||||
unpacking, reading or copying.
|
||||
|
||||
7. Additional Terms.
|
||||
|
||||
"Additional permissions" are terms that supplement the terms of this
|
||||
License by making exceptions from one or more of its conditions.
|
||||
Additional permissions that are applicable to the entire Program shall
|
||||
be treated as though they were included in this License, to the extent
|
||||
that they are valid under applicable law. If additional permissions
|
||||
apply only to part of the Program, that part may be used separately
|
||||
under those permissions, but the entire Program remains governed by
|
||||
this License without regard to the additional permissions.
|
||||
|
||||
When you convey a copy of a covered work, you may at your option
|
||||
remove any additional permissions from that copy, or from any part of
|
||||
it. (Additional permissions may be written to require their own
|
||||
removal in certain cases when you modify the work.) You may place
|
||||
additional permissions on material, added by you to a covered work,
|
||||
for which you have or can give appropriate copyright permission.
|
||||
|
||||
Notwithstanding any other provision of this License, for material you
|
||||
add to a covered work, you may (if authorized by the copyright holders of
|
||||
that material) supplement the terms of this License with terms:
|
||||
|
||||
a) Disclaiming warranty or limiting liability differently from the
|
||||
terms of sections 15 and 16 of this License; or
|
||||
|
||||
b) Requiring preservation of specified reasonable legal notices or
|
||||
author attributions in that material or in the Appropriate Legal
|
||||
Notices displayed by works containing it; or
|
||||
|
||||
c) Prohibiting misrepresentation of the origin of that material, or
|
||||
requiring that modified versions of such material be marked in
|
||||
reasonable ways as different from the original version; or
|
||||
|
||||
d) Limiting the use for publicity purposes of names of licensors or
|
||||
authors of the material; or
|
||||
|
||||
e) Declining to grant rights under trademark law for use of some
|
||||
trade names, trademarks, or service marks; or
|
||||
|
||||
f) Requiring indemnification of licensors and authors of that
|
||||
material by anyone who conveys the material (or modified versions of
|
||||
it) with contractual assumptions of liability to the recipient, for
|
||||
any liability that these contractual assumptions directly impose on
|
||||
those licensors and authors.
|
||||
|
||||
All other non-permissive additional terms are considered "further
|
||||
restrictions" within the meaning of section 10. If the Program as you
|
||||
received it, or any part of it, contains a notice stating that it is
|
||||
governed by this License along with a term that is a further
|
||||
restriction, you may remove that term. If a license document contains
|
||||
a further restriction but permits relicensing or conveying under this
|
||||
License, you may add to a covered work material governed by the terms
|
||||
of that license document, provided that the further restriction does
|
||||
not survive such relicensing or conveying.
|
||||
|
||||
If you add terms to a covered work in accord with this section, you
|
||||
must place, in the relevant source files, a statement of the
|
||||
additional terms that apply to those files, or a notice indicating
|
||||
where to find the applicable terms.
|
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the
|
||||
form of a separately written license, or stated as exceptions;
|
||||
the above requirements apply either way.
|
||||
|
||||
8. Termination.
|
||||
|
||||
You may not propagate or modify a covered work except as expressly
|
||||
provided under this License. Any attempt otherwise to propagate or
|
||||
modify it is void, and will automatically terminate your rights under
|
||||
this License (including any patent licenses granted under the third
|
||||
paragraph of section 11).
|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
license from a particular copyright holder is reinstated (a)
|
||||
provisionally, unless and until the copyright holder explicitly and
|
||||
finally terminates your license, and (b) permanently, if the copyright
|
||||
holder fails to notify you of the violation by some reasonable means
|
||||
prior to 60 days after the cessation.
|
||||
|
||||
Moreover, your license from a particular copyright holder is
|
||||
reinstated permanently if the copyright holder notifies you of the
|
||||
violation by some reasonable means, this is the first time you have
|
||||
received notice of violation of this License (for any work) from that
|
||||
copyright holder, and you cure the violation prior to 30 days after
|
||||
your receipt of the notice.
|
||||
|
||||
Termination of your rights under this section does not terminate the
|
||||
licenses of parties who have received copies or rights from you under
|
||||
this License. If your rights have been terminated and not permanently
|
||||
reinstated, you do not qualify to receive new licenses for the same
|
||||
material under section 10.
|
||||
|
||||
9. Acceptance Not Required for Having Copies.
|
||||
|
||||
You are not required to accept this License in order to receive or
|
||||
run a copy of the Program. Ancillary propagation of a covered work
|
||||
occurring solely as a consequence of using peer-to-peer transmission
|
||||
to receive a copy likewise does not require acceptance. However,
|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
not accept this License. Therefore, by modifying or propagating a
|
||||
covered work, you indicate your acceptance of this License to do so.
|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
for enforcing compliance by third parties with this License.
|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
organization, or substantially all assets of one, or subdividing an
|
||||
organization, or merging organizations. If propagation of a covered
|
||||
work results from an entity transaction, each party to that
|
||||
transaction who receives a copy of the work also receives whatever
|
||||
licenses to the work the party's predecessor in interest had or could
|
||||
give under the previous paragraph, plus a right to possession of the
|
||||
Corresponding Source of the work from the predecessor in interest, if
|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
rights granted under this License, and you may not initiate litigation
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
consequence of further modification of the contributor version. For
|
||||
purposes of this definition, "control" includes the right to grant
|
||||
patent sublicenses in a manner consistent with the requirements of
|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
patent license under the contributor's essential patent claims, to
|
||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||
propagate the contents of its contributor version.
|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
agreement or commitment, however denominated, not to enforce a patent
|
||||
(such as an express permission to practice a patent or covenant not to
|
||||
sue for patent infringement). To "grant" such a patent license to a
|
||||
party means to make such an agreement or commitment not to enforce a
|
||||
patent against the party.
|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
and the Corresponding Source of the work is not available for anyone
|
||||
to copy, free of charge and under the terms of this License, through a
|
||||
publicly available network server or other readily accessible means,
|
||||
then you must either (1) cause the Corresponding Source to be so
|
||||
available, or (2) arrange to deprive yourself of the benefit of the
|
||||
patent license for this particular work, or (3) arrange, in a manner
|
||||
consistent with the requirements of this License, to extend the patent
|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
actual knowledge that, but for the patent license, your conveying the
|
||||
covered work in a country, or your recipient's use of the covered work
|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
country that you have reason to believe are valid.
|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||
covered work, and grant a patent license to some of the parties
|
||||
receiving the covered work authorizing them to use, propagate, modify
|
||||
or convey a specific copy of the covered work, then the patent license
|
||||
you grant is automatically extended to all recipients of the covered
|
||||
work and works based on it.
|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
the scope of its coverage, prohibits the exercise of, or is
|
||||
conditioned on the non-exercise of one or more of the rights that are
|
||||
specifically granted under this License. You may not convey a covered
|
||||
work if you are a party to an arrangement with a third party that is
|
||||
in the business of distributing software, under which you make payment
|
||||
to the third party based on the extent of your activity of conveying
|
||||
the work, and under which the third party grants, to any of the
|
||||
parties who would receive the covered work from you, a discriminatory
|
||||
patent license (a) in connection with copies of the covered work
|
||||
conveyed by you (or copies made from those copies), or (b) primarily
|
||||
for and in connection with specific products or compilations that
|
||||
contain the covered work, unless you entered into that arrangement,
|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
otherwise be available to you under applicable patent law.
|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot convey a
|
||||
covered work so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you may
|
||||
not convey it at all. For example, if you agree to terms that obligate you
|
||||
to collect a royalty for further conveying from those to whom you convey
|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Remote Network Interaction; Use with the GNU General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, if you modify the
|
||||
Program, your modified version must prominently offer all users
|
||||
interacting with it remotely through a computer network (if your version
|
||||
supports such interaction) an opportunity to receive the Corresponding
|
||||
Source of your version by providing access to the Corresponding Source
|
||||
from a network server at no charge, through some standard or customary
|
||||
means of facilitating copying of software. This Corresponding Source
|
||||
shall include the Corresponding Source for any work covered by version 3
|
||||
of the GNU General Public License that is incorporated pursuant to the
|
||||
following paragraph.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
under version 3 of the GNU General Public License into a single
|
||||
combined work, and to convey the resulting work. The terms of this
|
||||
License will continue to apply to the part which is the covered work,
|
||||
but the work with which it is combined will remain governed by version
|
||||
3 of the GNU General Public License.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
the GNU Affero General Public License from time to time. Such new versions
|
||||
will be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
Program specifies that a certain numbered version of the GNU Affero General
|
||||
Public License "or any later version" applies to it, you have the
|
||||
option of following the terms and conditions either of that numbered
|
||||
version or of any later version published by the Free Software
|
||||
Foundation. If the Program does not specify a version number of the
|
||||
GNU Affero General Public License, you may choose any version ever published
|
||||
by the Free Software Foundation.
|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
versions of the GNU Affero General Public License can be used, that proxy's
|
||||
public statement of acceptance of a version permanently authorizes you
|
||||
to choose that version for the Program.
|
||||
|
||||
Later license versions may give you additional or different
|
||||
permissions. However, no additional obligations are imposed on any
|
||||
author or copyright holder as a result of your choosing to follow a
|
||||
later version.
|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
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/>.
|
||||
470
yolov5/README.md
Normal file
470
yolov5/README.md
Normal file
@ -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:
|
||||
|
||||
[](https://badge.fury.io/py/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 <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)
|
||||
```
|
||||
|
||||
|  |  |
|
||||
| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
|
||||
|
||||
### 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 <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
|
||||
470
yolov5/README.zh-CN.md
Normal file
470
yolov5/README.zh-CN.md
Normal file
@ -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：
|
||||
|
||||
[](https://badge.fury.io/py/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 <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)
|
||||
```
|
||||
|
||||
|  |  |
|
||||
| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
|
||||
|
||||
### 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 <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
|
||||
241
yolov5/classify/predict.py
Normal file
241
yolov5/classify/predict.py
Normal file
@ -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)
|
||||
382
yolov5/classify/train.py
Normal file
382
yolov5/classify/train.py
Normal file
@ -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)
|
||||
1488
yolov5/classify/tutorial.ipynb
vendored
Normal file
1488
yolov5/classify/tutorial.ipynb
vendored
Normal file
File diff suppressed because it is too large
Load Diff
178
yolov5/classify/val.py
Normal file
178
yolov5/classify/val.py
Normal file
@ -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)
|
||||
73
yolov5/data/Argoverse.yaml
Normal file
73
yolov5/data/Argoverse.yaml
Normal file
@ -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
|
||||
53
yolov5/data/GlobalWheat2020.yaml
Normal file
53
yolov5/data/GlobalWheat2020.yaml
Normal file
@ -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
|
||||
1021
yolov5/data/ImageNet.yaml
Normal file
1021
yolov5/data/ImageNet.yaml
Normal file
File diff suppressed because it is too large
Load Diff
31
yolov5/data/ImageNet10.yaml
Normal file
31
yolov5/data/ImageNet10.yaml
Normal file
@ -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
|
||||
120
yolov5/data/ImageNet100.yaml
Normal file
120
yolov5/data/ImageNet100.yaml
Normal file
@ -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
|
||||
1021
yolov5/data/ImageNet1000.yaml
Normal file
1021
yolov5/data/ImageNet1000.yaml
Normal file
File diff suppressed because it is too large
Load Diff
437
yolov5/data/Objects365.yaml
Normal file
437
yolov5/data/Objects365.yaml
Normal file
@ -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)
|
||||
52
yolov5/data/SKU-110K.yaml
Normal file
52
yolov5/data/SKU-110K.yaml
Normal file
@ -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
|
||||
99
yolov5/data/VOC.yaml
Normal file
99
yolov5/data/VOC.yaml
Normal file
@ -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
|
||||
69
yolov5/data/VisDrone.yaml
Normal file
69
yolov5/data/VisDrone.yaml
Normal file
@ -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
|
||||
115
yolov5/data/coco.yaml
Normal file
115
yolov5/data/coco.yaml
Normal file
@ -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)
|
||||
100
yolov5/data/coco128-seg.yaml
Normal file
100
yolov5/data/coco128-seg.yaml
Normal file
@ -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
|
||||
100
yolov5/data/coco128.yaml
Normal file
100
yolov5/data/coco128.yaml
Normal file
@ -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
|
||||
BIN
yolov5/data/images/bus.jpg
Normal file
BIN
yolov5/data/images/bus.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 476 KiB |
BIN
yolov5/data/images/zidane.jpg
Normal file
BIN
yolov5/data/images/zidane.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 165 KiB |
56
yolov5/data/scripts/get_coco.sh
Normal file
56
yolov5/data/scripts/get_coco.sh
Normal file
@ -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
|
||||
17
yolov5/data/scripts/get_coco128.sh
Normal file
17
yolov5/data/scripts/get_coco128.sh
Normal file
@ -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 $f &
|
||||
|
||||
wait # finish background tasks
|
||||
51
yolov5/data/scripts/get_imagenet.sh
Normal file
51
yolov5/data/scripts/get_imagenet.sh
Normal file
@ -0,0 +1,51 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# Download ILSVRC2012 ImageNet dataset https://image-net.org
|
||||
# Example usage: bash data/scripts/get_imagenet.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet ← downloads here
|
||||
|
||||
# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
|
||||
if [ "$#" -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 [ "$val" == "true" ]; then
|
||||
wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
|
||||
mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
|
||||
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
|
||||
fi
|
||||
|
||||
# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
|
||||
# rm train/n04266014/n04266014_10835.JPEG
|
||||
|
||||
# TFRecords (optional)
|
||||
# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt
|
||||
29
yolov5/data/scripts/get_imagenet10.sh
Normal file
29
yolov5/data/scripts/get_imagenet10.sh
Normal file
@ -0,0 +1,29 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# Download ILSVRC2012 ImageNet dataset https://image-net.org
|
||||
# Example usage: bash data/scripts/get_imagenet.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet ← downloads here
|
||||
|
||||
# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
|
||||
if [ "$#" -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 $d
|
||||
|
||||
# Download/unzip train
|
||||
wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet10.zip
|
||||
unzip imagenet10.zip && rm imagenet10.zip
|
||||
29
yolov5/data/scripts/get_imagenet100.sh
Normal file
29
yolov5/data/scripts/get_imagenet100.sh
Normal file
@ -0,0 +1,29 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# Download ILSVRC2012 ImageNet dataset https://image-net.org
|
||||
# Example usage: bash data/scripts/get_imagenet.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet ← downloads here
|
||||
|
||||
# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
|
||||
if [ "$#" -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 $d
|
||||
|
||||
# Download/unzip train
|
||||
wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet100.zip
|
||||
unzip imagenet100.zip && rm imagenet100.zip
|
||||
29
yolov5/data/scripts/get_imagenet1000.sh
Normal file
29
yolov5/data/scripts/get_imagenet1000.sh
Normal file
@ -0,0 +1,29 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
|
||||
# Download ILSVRC2012 ImageNet dataset https://image-net.org
|
||||
# Example usage: bash data/scripts/get_imagenet.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet ← downloads here
|
||||
|
||||
# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
|
||||
if [ "$#" -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
|
||||
152
yolov5/data/xView.yaml
Normal file
152
yolov5/data/xView.yaml
Normal file
@ -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')
|
||||
307
yolov5/segment/predict.py
Normal file
307
yolov5/segment/predict.py
Normal file
@ -0,0 +1,307 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.
|
||||
|
||||
Usage - sources:
|
||||
$ python segment/predict.py --weights yolov5s-seg.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 segment/predict.py --weights yolov5s-seg.pt # PyTorch
|
||||
yolov5s-seg.torchscript # TorchScript
|
||||
yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s-seg_openvino_model # OpenVINO
|
||||
yolov5s-seg.engine # TensorRT
|
||||
yolov5s-seg.mlmodel # CoreML (macOS-only)
|
||||
yolov5s-seg_saved_model # TensorFlow SavedModel
|
||||
yolov5s-seg.pb # TensorFlow GraphDef
|
||||
yolov5s-seg.tflite # TensorFlow Lite
|
||||
yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s-seg_paddle_model # PaddlePaddle
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
|
||||
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, 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,
|
||||
scale_segments,
|
||||
strip_optimizer,
|
||||
)
|
||||
from utils.segment.general import masks2segments, process_mask, process_mask_native
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
weights=ROOT / "yolov5s-seg.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=(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_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/predict-seg", # 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
|
||||
retina_masks=False,
|
||||
):
|
||||
"""Run YOLOv5 segmentation inference on diverse sources including images, videos, directories, and streams."""
|
||||
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 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
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||||
pred, proto = model(im, augment=augment, visualize=visualize)[:2]
|
||||
|
||||
# NMS
|
||||
with dt[2]:
|
||||
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32)
|
||||
|
||||
# Second-stage classifier (optional)
|
||||
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
|
||||
|
||||
# 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
|
||||
imc = im0.copy() if save_crop else im0 # for save_crop
|
||||
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
|
||||
if len(det):
|
||||
if retina_masks:
|
||||
# scale bbox first the crop masks
|
||||
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
|
||||
masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC
|
||||
else:
|
||||
masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC
|
||||
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
|
||||
|
||||
# Segments
|
||||
if save_txt:
|
||||
segments = [
|
||||
scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
|
||||
for x in reversed(masks2segments(masks))
|
||||
]
|
||||
|
||||
# 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
|
||||
|
||||
# Mask plotting
|
||||
annotator.masks(
|
||||
masks,
|
||||
colors=[colors(x, True) for x in det[:, 5]],
|
||||
im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous()
|
||||
/ 255
|
||||
if retina_masks
|
||||
else im[i],
|
||||
)
|
||||
|
||||
# Write results
|
||||
for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
|
||||
if save_txt: # Write to file
|
||||
seg = segments[j].reshape(-1) # (n,2) to (n*2)
|
||||
line = (cls, *seg, conf) if save_conf else (cls, *seg) # 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))
|
||||
# annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
|
||||
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)
|
||||
if cv2.waitKey(1) == ord("q"): # 1 millisecond
|
||||
exit()
|
||||
|
||||
# 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():
|
||||
"""Parses command-line options for YOLOv5 inference including model paths, data sources, inference settings, and
|
||||
output preferences.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.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=[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-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/predict-seg", 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")
|
||||
parser.add_argument("--retina-masks", action="store_true", help="whether to plot masks in native resolution")
|
||||
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 given options, checking for requirements before launching."""
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
764
yolov5/segment/train.py
Normal file
764
yolov5/segment/train.py
Normal file
@ -0,0 +1,764 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Train a YOLOv5 segment model on a segment dataset Models and datasets download automatically from the latest YOLOv5
|
||||
release.
|
||||
|
||||
Usage - Single-GPU training:
|
||||
$ python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # from pretrained (recommended)
|
||||
$ python segment/train.py --data coco128-seg.yaml --weights '' --cfg yolov5s-seg.yaml --img 640 # from scratch
|
||||
|
||||
Usage - Multi-GPU DDP training:
|
||||
$ 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
|
||||
|
||||
Models: https://github.com/ultralytics/yolov5/tree/master/models
|
||||
Datasets: https://github.com/ultralytics/yolov5/tree/master/data
|
||||
Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from copy import deepcopy
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import yaml
|
||||
from torch.optim import lr_scheduler
|
||||
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
|
||||
|
||||
import segment.val as validate # for end-of-epoch mAP
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import SegmentationModel
|
||||
from utils.autoanchor import check_anchors
|
||||
from utils.autobatch import check_train_batch_size
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.downloads import attempt_download, is_url
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
TQDM_BAR_FORMAT,
|
||||
check_amp,
|
||||
check_dataset,
|
||||
check_file,
|
||||
check_git_info,
|
||||
check_git_status,
|
||||
check_img_size,
|
||||
check_requirements,
|
||||
check_suffix,
|
||||
check_yaml,
|
||||
colorstr,
|
||||
get_latest_run,
|
||||
increment_path,
|
||||
init_seeds,
|
||||
intersect_dicts,
|
||||
labels_to_class_weights,
|
||||
labels_to_image_weights,
|
||||
one_cycle,
|
||||
print_args,
|
||||
print_mutation,
|
||||
strip_optimizer,
|
||||
yaml_save,
|
||||
)
|
||||
from utils.loggers import GenericLogger
|
||||
from utils.plots import plot_evolve, plot_labels
|
||||
from utils.segment.dataloaders import create_dataloader
|
||||
from utils.segment.loss import ComputeLoss
|
||||
from utils.segment.metrics import KEYS, fitness
|
||||
from utils.segment.plots import plot_images_and_masks, plot_results_with_masks
|
||||
from utils.torch_utils import (
|
||||
EarlyStopping,
|
||||
ModelEMA,
|
||||
de_parallel,
|
||||
select_device,
|
||||
smart_DDP,
|
||||
smart_optimizer,
|
||||
smart_resume,
|
||||
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(hyp, opt, device, callbacks):
|
||||
"""
|
||||
Trains the YOLOv5 model on a dataset, managing hyperparameters, model optimization, logging, and validation.
|
||||
|
||||
`hyp` is path/to/hyp.yaml or hyp dictionary.
|
||||
"""
|
||||
(
|
||||
save_dir,
|
||||
epochs,
|
||||
batch_size,
|
||||
weights,
|
||||
single_cls,
|
||||
evolve,
|
||||
data,
|
||||
cfg,
|
||||
resume,
|
||||
noval,
|
||||
nosave,
|
||||
workers,
|
||||
freeze,
|
||||
mask_ratio,
|
||||
) = (
|
||||
Path(opt.save_dir),
|
||||
opt.epochs,
|
||||
opt.batch_size,
|
||||
opt.weights,
|
||||
opt.single_cls,
|
||||
opt.evolve,
|
||||
opt.data,
|
||||
opt.cfg,
|
||||
opt.resume,
|
||||
opt.noval,
|
||||
opt.nosave,
|
||||
opt.workers,
|
||||
opt.freeze,
|
||||
opt.mask_ratio,
|
||||
)
|
||||
# callbacks.run('on_pretrain_routine_start')
|
||||
|
||||
# Directories
|
||||
w = save_dir / "weights" # weights dir
|
||||
(w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
|
||||
last, best = w / "last.pt", w / "best.pt"
|
||||
|
||||
# Hyperparameters
|
||||
if isinstance(hyp, str):
|
||||
with open(hyp, errors="ignore") as f:
|
||||
hyp = yaml.safe_load(f) # load hyps dict
|
||||
LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items()))
|
||||
opt.hyp = hyp.copy() # for saving hyps to checkpoints
|
||||
|
||||
# Save run settings
|
||||
if not evolve:
|
||||
yaml_save(save_dir / "hyp.yaml", hyp)
|
||||
yaml_save(save_dir / "opt.yaml", vars(opt))
|
||||
|
||||
# Loggers
|
||||
data_dict = None
|
||||
if RANK in {-1, 0}:
|
||||
logger = GenericLogger(opt=opt, console_logger=LOGGER)
|
||||
|
||||
# Config
|
||||
plots = not evolve and not opt.noplots # create plots
|
||||
overlap = not opt.no_overlap
|
||||
cuda = device.type != "cpu"
|
||||
init_seeds(opt.seed + 1 + RANK, deterministic=True)
|
||||
with torch_distributed_zero_first(LOCAL_RANK):
|
||||
data_dict = data_dict or check_dataset(data) # check if None
|
||||
train_path, val_path = data_dict["train"], data_dict["val"]
|
||||
nc = 1 if single_cls else int(data_dict["nc"]) # number of classes
|
||||
names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names
|
||||
is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset
|
||||
|
||||
# Model
|
||||
check_suffix(weights, ".pt") # check weights
|
||||
pretrained = weights.endswith(".pt")
|
||||
if pretrained:
|
||||
with torch_distributed_zero_first(LOCAL_RANK):
|
||||
weights = attempt_download(weights) # download if not found locally
|
||||
ckpt = torch.load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak
|
||||
model = SegmentationModel(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device)
|
||||
exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys
|
||||
csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32
|
||||
csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
|
||||
model.load_state_dict(csd, strict=False) # load
|
||||
LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report
|
||||
else:
|
||||
model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create
|
||||
amp = check_amp(model) # check AMP
|
||||
|
||||
# Freeze
|
||||
freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze
|
||||
for k, v in model.named_parameters():
|
||||
v.requires_grad = True # train all layers
|
||||
# v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results)
|
||||
if any(x in k for x in freeze):
|
||||
LOGGER.info(f"freezing {k}")
|
||||
v.requires_grad = False
|
||||
|
||||
# Image size
|
||||
gs = max(int(model.stride.max()), 32) # grid size (max stride)
|
||||
imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
|
||||
|
||||
# Batch size
|
||||
if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
|
||||
batch_size = check_train_batch_size(model, imgsz, amp)
|
||||
logger.update_params({"batch_size": batch_size})
|
||||
# loggers.on_params_update({"batch_size": batch_size})
|
||||
|
||||
# Optimizer
|
||||
nbs = 64 # nominal batch size
|
||||
accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
|
||||
hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay
|
||||
optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"])
|
||||
|
||||
# Scheduler
|
||||
if opt.cos_lr:
|
||||
lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf']
|
||||
else:
|
||||
|
||||
def lf(x):
|
||||
"""Linear learning rate scheduler decreasing from 1 to hyp['lrf'] over 'epochs'."""
|
||||
return (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear
|
||||
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
|
||||
|
||||
# EMA
|
||||
ema = ModelEMA(model) if RANK in {-1, 0} else None
|
||||
|
||||
# Resume
|
||||
best_fitness, start_epoch = 0.0, 0
|
||||
if pretrained:
|
||||
if resume:
|
||||
best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
|
||||
del ckpt, csd
|
||||
|
||||
# DP mode
|
||||
if cuda and RANK == -1 and torch.cuda.device_count() > 1:
|
||||
LOGGER.warning(
|
||||
"WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n"
|
||||
"See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started."
|
||||
)
|
||||
model = torch.nn.DataParallel(model)
|
||||
|
||||
# SyncBatchNorm
|
||||
if opt.sync_bn and cuda and RANK != -1:
|
||||
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
|
||||
LOGGER.info("Using SyncBatchNorm()")
|
||||
|
||||
# Trainloader
|
||||
train_loader, dataset = create_dataloader(
|
||||
train_path,
|
||||
imgsz,
|
||||
batch_size // WORLD_SIZE,
|
||||
gs,
|
||||
single_cls,
|
||||
hyp=hyp,
|
||||
augment=True,
|
||||
cache=None if opt.cache == "val" else opt.cache,
|
||||
rect=opt.rect,
|
||||
rank=LOCAL_RANK,
|
||||
workers=workers,
|
||||
image_weights=opt.image_weights,
|
||||
quad=opt.quad,
|
||||
prefix=colorstr("train: "),
|
||||
shuffle=True,
|
||||
mask_downsample_ratio=mask_ratio,
|
||||
overlap_mask=overlap,
|
||||
)
|
||||
labels = np.concatenate(dataset.labels, 0)
|
||||
mlc = int(labels[:, 0].max()) # max label class
|
||||
assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}"
|
||||
|
||||
# Process 0
|
||||
if RANK in {-1, 0}:
|
||||
val_loader = create_dataloader(
|
||||
val_path,
|
||||
imgsz,
|
||||
batch_size // WORLD_SIZE * 2,
|
||||
gs,
|
||||
single_cls,
|
||||
hyp=hyp,
|
||||
cache=None if noval else opt.cache,
|
||||
rect=True,
|
||||
rank=-1,
|
||||
workers=workers * 2,
|
||||
pad=0.5,
|
||||
mask_downsample_ratio=mask_ratio,
|
||||
overlap_mask=overlap,
|
||||
prefix=colorstr("val: "),
|
||||
)[0]
|
||||
|
||||
if not resume:
|
||||
if not opt.noautoanchor:
|
||||
check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor
|
||||
model.half().float() # pre-reduce anchor precision
|
||||
|
||||
if plots:
|
||||
plot_labels(labels, names, save_dir)
|
||||
# callbacks.run('on_pretrain_routine_end', labels, names)
|
||||
|
||||
# DDP mode
|
||||
if cuda and RANK != -1:
|
||||
model = smart_DDP(model)
|
||||
|
||||
# Model attributes
|
||||
nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)
|
||||
hyp["box"] *= 3 / nl # scale to layers
|
||||
hyp["cls"] *= nc / 80 * 3 / nl # scale to classes and layers
|
||||
hyp["obj"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
|
||||
hyp["label_smoothing"] = opt.label_smoothing
|
||||
model.nc = nc # attach number of classes to model
|
||||
model.hyp = hyp # attach hyperparameters to model
|
||||
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
|
||||
model.names = names
|
||||
|
||||
# Start training
|
||||
t0 = time.time()
|
||||
nb = len(train_loader) # number of batches
|
||||
nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations)
|
||||
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
|
||||
last_opt_step = -1
|
||||
maps = np.zeros(nc) # mAP per class
|
||||
results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
|
||||
scheduler.last_epoch = start_epoch - 1 # do not move
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=amp)
|
||||
stopper, stop = EarlyStopping(patience=opt.patience), False
|
||||
compute_loss = ComputeLoss(model, overlap=overlap) # init loss class
|
||||
# callbacks.run('on_train_start')
|
||||
LOGGER.info(
|
||||
f"Image sizes {imgsz} train, {imgsz} val\n"
|
||||
f"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n"
|
||||
f"Logging results to {colorstr('bold', save_dir)}\n"
|
||||
f"Starting training for {epochs} epochs..."
|
||||
)
|
||||
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
|
||||
# callbacks.run('on_train_epoch_start')
|
||||
model.train()
|
||||
|
||||
# Update image weights (optional, single-GPU only)
|
||||
if opt.image_weights:
|
||||
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
|
||||
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
|
||||
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
|
||||
|
||||
# Update mosaic border (optional)
|
||||
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
|
||||
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
|
||||
|
||||
mloss = torch.zeros(4, device=device) # mean losses
|
||||
if RANK != -1:
|
||||
train_loader.sampler.set_epoch(epoch)
|
||||
pbar = enumerate(train_loader)
|
||||
LOGGER.info(
|
||||
("\n" + "%11s" * 8)
|
||||
% ("Epoch", "GPU_mem", "box_loss", "seg_loss", "obj_loss", "cls_loss", "Instances", "Size")
|
||||
)
|
||||
if RANK in {-1, 0}:
|
||||
pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
|
||||
optimizer.zero_grad()
|
||||
for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------
|
||||
# callbacks.run('on_train_batch_start')
|
||||
ni = i + nb * epoch # number integrated batches (since train start)
|
||||
imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0
|
||||
|
||||
# Warmup
|
||||
if ni <= nw:
|
||||
xi = [0, nw] # x interp
|
||||
# compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
|
||||
accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
|
||||
for j, x in enumerate(optimizer.param_groups):
|
||||
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
|
||||
x["lr"] = np.interp(ni, xi, [hyp["warmup_bias_lr"] if j == 0 else 0.0, x["initial_lr"] * lf(epoch)])
|
||||
if "momentum" in x:
|
||||
x["momentum"] = np.interp(ni, xi, [hyp["warmup_momentum"], hyp["momentum"]])
|
||||
|
||||
# Multi-scale
|
||||
if opt.multi_scale:
|
||||
sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs # size
|
||||
sf = sz / max(imgs.shape[2:]) # scale factor
|
||||
if sf != 1:
|
||||
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
|
||||
imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False)
|
||||
|
||||
# Forward
|
||||
with torch.cuda.amp.autocast(amp):
|
||||
pred = model(imgs) # forward
|
||||
loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float())
|
||||
if RANK != -1:
|
||||
loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
|
||||
if opt.quad:
|
||||
loss *= 4.0
|
||||
|
||||
# Backward
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
|
||||
if ni - last_opt_step >= accumulate:
|
||||
scaler.unscale_(optimizer) # unscale gradients
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
|
||||
scaler.step(optimizer) # optimizer.step
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
if ema:
|
||||
ema.update(model)
|
||||
last_opt_step = ni
|
||||
|
||||
# Log
|
||||
if RANK in {-1, 0}:
|
||||
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
||||
mem = f"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G" # (GB)
|
||||
pbar.set_description(
|
||||
("%11s" * 2 + "%11.4g" * 6)
|
||||
% (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1])
|
||||
)
|
||||
# callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths)
|
||||
# if callbacks.stop_training:
|
||||
# return
|
||||
|
||||
# Mosaic plots
|
||||
if plots:
|
||||
if ni < 3:
|
||||
plot_images_and_masks(imgs, targets, masks, paths, save_dir / f"train_batch{ni}.jpg")
|
||||
if ni == 10:
|
||||
files = sorted(save_dir.glob("train*.jpg"))
|
||||
logger.log_images(files, "Mosaics", epoch)
|
||||
# end batch ------------------------------------------------------------------------------------------------
|
||||
|
||||
# Scheduler
|
||||
lr = [x["lr"] for x in optimizer.param_groups] # for loggers
|
||||
scheduler.step()
|
||||
|
||||
if RANK in {-1, 0}:
|
||||
# mAP
|
||||
# callbacks.run('on_train_epoch_end', epoch=epoch)
|
||||
ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"])
|
||||
final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
|
||||
if not noval or final_epoch: # Calculate mAP
|
||||
results, maps, _ = validate.run(
|
||||
data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
half=amp,
|
||||
model=ema.ema,
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
plots=False,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss,
|
||||
mask_downsample_ratio=mask_ratio,
|
||||
overlap=overlap,
|
||||
)
|
||||
|
||||
# Update best mAP
|
||||
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
|
||||
stop = stopper(epoch=epoch, fitness=fi) # early stop check
|
||||
if fi > best_fitness:
|
||||
best_fitness = fi
|
||||
log_vals = list(mloss) + list(results) + lr
|
||||
# callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
|
||||
# Log val metrics and media
|
||||
metrics_dict = dict(zip(KEYS, log_vals))
|
||||
logger.log_metrics(metrics_dict, epoch)
|
||||
|
||||
# Save model
|
||||
if (not nosave) or (final_epoch and not evolve): # if save
|
||||
ckpt = {
|
||||
"epoch": epoch,
|
||||
"best_fitness": best_fitness,
|
||||
"model": deepcopy(de_parallel(model)).half(),
|
||||
"ema": deepcopy(ema.ema).half(),
|
||||
"updates": ema.updates,
|
||||
"optimizer": 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 == fi:
|
||||
torch.save(ckpt, best)
|
||||
if opt.save_period > 0 and epoch % opt.save_period == 0:
|
||||
torch.save(ckpt, w / f"epoch{epoch}.pt")
|
||||
logger.log_model(w / f"epoch{epoch}.pt")
|
||||
del ckpt
|
||||
# callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
|
||||
|
||||
# EarlyStopping
|
||||
if RANK != -1: # if DDP training
|
||||
broadcast_list = [stop if RANK == 0 else None]
|
||||
dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks
|
||||
if RANK != 0:
|
||||
stop = broadcast_list[0]
|
||||
if stop:
|
||||
break # must break all DDP ranks
|
||||
|
||||
# end epoch ----------------------------------------------------------------------------------------------------
|
||||
# end training -----------------------------------------------------------------------------------------------------
|
||||
if RANK in {-1, 0}:
|
||||
LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.")
|
||||
for f in last, best:
|
||||
if f.exists():
|
||||
strip_optimizer(f) # strip optimizers
|
||||
if f is best:
|
||||
LOGGER.info(f"\nValidating {f}...")
|
||||
results, _, _ = validate.run(
|
||||
data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
model=attempt_load(f, device).half(),
|
||||
iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
save_json=is_coco,
|
||||
verbose=True,
|
||||
plots=plots,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss,
|
||||
mask_downsample_ratio=mask_ratio,
|
||||
overlap=overlap,
|
||||
) # val best model with plots
|
||||
if is_coco:
|
||||
# callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
|
||||
metrics_dict = dict(zip(KEYS, list(mloss) + list(results) + lr))
|
||||
logger.log_metrics(metrics_dict, epoch)
|
||||
|
||||
# callbacks.run('on_train_end', last, best, epoch, results)
|
||||
# on train end callback using genericLogger
|
||||
logger.log_metrics(dict(zip(KEYS[4:16], results)), epochs)
|
||||
if not opt.evolve:
|
||||
logger.log_model(best, epoch)
|
||||
if plots:
|
||||
plot_results_with_masks(file=save_dir / "results.csv") # save results.png
|
||||
files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))]
|
||||
files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
|
||||
logger.log_images(files, "Results", epoch + 1)
|
||||
logger.log_images(sorted(save_dir.glob("val*.jpg")), "Validation", epoch + 1)
|
||||
torch.cuda.empty_cache()
|
||||
return results
|
||||
|
||||
|
||||
def parse_opt(known=False):
|
||||
"""
|
||||
Parses command line arguments for training configurations, returning parsed arguments.
|
||||
|
||||
Supports both known and unknown args.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", type=str, default=ROOT / "yolov5s-seg.pt", help="initial weights path")
|
||||
parser.add_argument("--cfg", type=str, default="", help="model.yaml path")
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path")
|
||||
parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path")
|
||||
parser.add_argument("--epochs", type=int, default=100, help="total training epochs")
|
||||
parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)")
|
||||
parser.add_argument("--rect", action="store_true", help="rectangular training")
|
||||
parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training")
|
||||
parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
|
||||
parser.add_argument("--noval", action="store_true", help="only validate final epoch")
|
||||
parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor")
|
||||
parser.add_argument("--noplots", action="store_true", help="save no plot files")
|
||||
parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations")
|
||||
parser.add_argument("--bucket", type=str, default="", help="gsutil bucket")
|
||||
parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk")
|
||||
parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%")
|
||||
parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class")
|
||||
parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer")
|
||||
parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode")
|
||||
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-seg", 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("--quad", action="store_true", help="quad dataloader")
|
||||
parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler")
|
||||
parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon")
|
||||
parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)")
|
||||
parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2")
|
||||
parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)")
|
||||
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")
|
||||
|
||||
# Instance Segmentation Args
|
||||
parser.add_argument("--mask-ratio", type=int, default=4, help="Downsample the truth masks to saving memory")
|
||||
parser.add_argument("--no-overlap", action="store_true", help="Overlap masks train faster at slightly less mAP")
|
||||
|
||||
return parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
|
||||
|
||||
def main(opt, callbacks=Callbacks()):
|
||||
"""Initializes training or evolution of YOLOv5 models based on provided configuration and options."""
|
||||
if RANK in {-1, 0}:
|
||||
print_args(vars(opt))
|
||||
check_git_status()
|
||||
check_requirements(ROOT / "requirements.txt")
|
||||
|
||||
# Resume
|
||||
if opt.resume and not opt.evolve: # resume from specified or most recent last.pt
|
||||
last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
|
||||
opt_yaml = last.parent.parent / "opt.yaml" # train options yaml
|
||||
opt_data = opt.data # original dataset
|
||||
if opt_yaml.is_file():
|
||||
with open(opt_yaml, errors="ignore") as f:
|
||||
d = yaml.safe_load(f)
|
||||
else:
|
||||
d = torch.load(last, map_location="cpu")["opt"]
|
||||
opt = argparse.Namespace(**d) # replace
|
||||
opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate
|
||||
if is_url(opt_data):
|
||||
opt.data = check_file(opt_data) # avoid HUB resume auth timeout
|
||||
else:
|
||||
opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = (
|
||||
check_file(opt.data),
|
||||
check_yaml(opt.cfg),
|
||||
check_yaml(opt.hyp),
|
||||
str(opt.weights),
|
||||
str(opt.project),
|
||||
) # checks
|
||||
assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified"
|
||||
if opt.evolve:
|
||||
if opt.project == str(ROOT / "runs/train-seg"): # if default project name, rename to runs/evolve-seg
|
||||
opt.project = str(ROOT / "runs/evolve-seg")
|
||||
opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
|
||||
if opt.name == "cfg":
|
||||
opt.name = Path(opt.cfg).stem # use model.yaml as name
|
||||
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
|
||||
|
||||
# DDP mode
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if LOCAL_RANK != -1:
|
||||
msg = "is not compatible with YOLOv5 Multi-GPU DDP training"
|
||||
assert not opt.image_weights, f"--image-weights {msg}"
|
||||
assert not opt.evolve, f"--evolve {msg}"
|
||||
assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, 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")
|
||||
|
||||
# Train
|
||||
if not opt.evolve:
|
||||
train(opt.hyp, opt, device, callbacks)
|
||||
|
||||
# Evolve hyperparameters (optional)
|
||||
else:
|
||||
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
|
||||
meta = {
|
||||
"lr0": (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
"lrf": (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
|
||||
"momentum": (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
|
||||
"weight_decay": (1, 0.0, 0.001), # optimizer weight decay
|
||||
"warmup_epochs": (1, 0.0, 5.0), # warmup epochs (fractions ok)
|
||||
"warmup_momentum": (1, 0.0, 0.95), # warmup initial momentum
|
||||
"warmup_bias_lr": (1, 0.0, 0.2), # warmup initial bias lr
|
||||
"box": (1, 0.02, 0.2), # box loss gain
|
||||
"cls": (1, 0.2, 4.0), # cls loss gain
|
||||
"cls_pw": (1, 0.5, 2.0), # cls BCELoss positive_weight
|
||||
"obj": (1, 0.2, 4.0), # obj loss gain (scale with pixels)
|
||||
"obj_pw": (1, 0.5, 2.0), # obj BCELoss positive_weight
|
||||
"iou_t": (0, 0.1, 0.7), # IoU training threshold
|
||||
"anchor_t": (1, 2.0, 8.0), # anchor-multiple threshold
|
||||
"anchors": (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
|
||||
"fl_gamma": (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
|
||||
"hsv_h": (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
|
||||
"hsv_s": (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
|
||||
"hsv_v": (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
|
||||
"degrees": (1, 0.0, 45.0), # image rotation (+/- deg)
|
||||
"translate": (1, 0.0, 0.9), # image translation (+/- fraction)
|
||||
"scale": (1, 0.0, 0.9), # image scale (+/- gain)
|
||||
"shear": (1, 0.0, 10.0), # image shear (+/- deg)
|
||||
"perspective": (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
|
||||
"flipud": (1, 0.0, 1.0), # image flip up-down (probability)
|
||||
"fliplr": (0, 0.0, 1.0), # image flip left-right (probability)
|
||||
"mosaic": (1, 0.0, 1.0), # image mixup (probability)
|
||||
"mixup": (1, 0.0, 1.0), # image mixup (probability)
|
||||
"copy_paste": (1, 0.0, 1.0),
|
||||
} # segment copy-paste (probability)
|
||||
|
||||
with open(opt.hyp, errors="ignore") as f:
|
||||
hyp = yaml.safe_load(f) # load hyps dict
|
||||
if "anchors" not in hyp: # anchors commented in hyp.yaml
|
||||
hyp["anchors"] = 3
|
||||
if opt.noautoanchor:
|
||||
del hyp["anchors"], meta["anchors"]
|
||||
opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
|
||||
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
|
||||
evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv"
|
||||
if opt.bucket:
|
||||
# download evolve.csv if exists
|
||||
subprocess.run(
|
||||
[
|
||||
"gsutil",
|
||||
"cp",
|
||||
f"gs://{opt.bucket}/evolve.csv",
|
||||
str(evolve_csv),
|
||||
]
|
||||
)
|
||||
|
||||
for _ in range(opt.evolve): # generations to evolve
|
||||
if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
|
||||
# Select parent(s)
|
||||
parent = "single" # parent selection method: 'single' or 'weighted'
|
||||
x = np.loadtxt(evolve_csv, ndmin=2, delimiter=",", skiprows=1)
|
||||
n = min(5, len(x)) # number of previous results to consider
|
||||
x = x[np.argsort(-fitness(x))][:n] # top n mutations
|
||||
w = fitness(x) - fitness(x).min() + 1e-6 # weights (sum > 0)
|
||||
if parent == "single" or len(x) == 1:
|
||||
# x = x[random.randint(0, n - 1)] # random selection
|
||||
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
|
||||
elif parent == "weighted":
|
||||
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
|
||||
|
||||
# Mutate
|
||||
mp, s = 0.8, 0.2 # mutation probability, sigma
|
||||
npr = np.random
|
||||
npr.seed(int(time.time()))
|
||||
g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1
|
||||
ng = len(meta)
|
||||
v = np.ones(ng)
|
||||
while all(v == 1): # mutate until a change occurs (prevent duplicates)
|
||||
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
|
||||
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
|
||||
hyp[k] = float(x[i + 12] * v[i]) # mutate
|
||||
|
||||
# Constrain to limits
|
||||
for k, v in meta.items():
|
||||
hyp[k] = max(hyp[k], v[1]) # lower limit
|
||||
hyp[k] = min(hyp[k], v[2]) # upper limit
|
||||
hyp[k] = round(hyp[k], 5) # significant digits
|
||||
|
||||
# Train mutation
|
||||
results = train(hyp.copy(), opt, device, callbacks)
|
||||
callbacks = Callbacks()
|
||||
# Write mutation results
|
||||
print_mutation(KEYS[4:16], results, hyp.copy(), save_dir, opt.bucket)
|
||||
|
||||
# Plot results
|
||||
plot_evolve(evolve_csv)
|
||||
LOGGER.info(
|
||||
f"Hyperparameter evolution finished {opt.evolve} generations\n"
|
||||
f"Results saved to {colorstr('bold', save_dir)}\n"
|
||||
f"Usage example: $ python train.py --hyp {evolve_yaml}"
|
||||
)
|
||||
|
||||
|
||||
def run(**kwargs):
|
||||
"""
|
||||
Executes YOLOv5 training with given parameters, altering options programmatically; returns updated options.
|
||||
|
||||
Example: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
|
||||
"""
|
||||
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)
|
||||
602
yolov5/segment/tutorial.ipynb
vendored
Normal file
602
yolov5/segment/tutorial.ipynb
vendored
Normal file
@ -0,0 +1,602 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "t6MPjfT5NrKQ"
|
||||
},
|
||||
"source": [
|
||||
"<div align=\"center\">\n",
|
||||
"\n",
|
||||
" <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
|
||||
" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png\"></a>\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"<br>\n",
|
||||
" <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a>\n",
|
||||
" <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>\n",
|
||||
" <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>\n",
|
||||
"<br>\n",
|
||||
"\n",
|
||||
"This <a href=\"https://github.com/ultralytics/yolov5\">YOLOv5</a> 🚀 notebook by <a href=\"https://ultralytics.com\">Ultralytics</a> presents simple train, validate and predict examples to help start your AI adventure.<br>See <a href=\"https://github.com/ultralytics/yolov5/issues/new/choose\">GitHub</a> for community support or <a href=\"https://ultralytics.com/contact\">contact us</a> for professional support.\n",
|
||||
"\n",
|
||||
"</div>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "7mGmQbAO5pQb"
|
||||
},
|
||||
"source": [
|
||||
"# Setup\n",
|
||||
"\n",
|
||||
"Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "wbvMlHd_QwMG",
|
||||
"outputId": "171b23f0-71b9-4cbf-b666-6fa2ecef70c8"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!git clone https://github.com/ultralytics/yolov5 # clone\n",
|
||||
"%cd yolov5\n",
|
||||
"%pip install -qr requirements.txt comet_ml # install\n",
|
||||
"\n",
|
||||
"import torch\n",
|
||||
"\n",
|
||||
"import utils\n",
|
||||
"\n",
|
||||
"display = utils.notebook_init() # checks"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "4JnkELT0cIJg"
|
||||
},
|
||||
"source": [
|
||||
"# 1. Predict\n",
|
||||
"\n",
|
||||
"`segment/predict.py` runs YOLOv5 instance segmentation inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict`. Example inference sources are:\n",
|
||||
"\n",
|
||||
"```shell\n",
|
||||
"python segment/predict.py --source 0 # webcam\n",
|
||||
" img.jpg # image \n",
|
||||
" vid.mp4 # video\n",
|
||||
" screen # screenshot\n",
|
||||
" path/ # directory\n",
|
||||
" 'path/*.jpg' # glob\n",
|
||||
" 'https://youtu.be/LNwODJXcvt4' # YouTube\n",
|
||||
" 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "zR9ZbuQCH7FX",
|
||||
"outputId": "3f67f1c7-f15e-4fa5-d251-967c3b77eaad"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1msegment/predict: \u001b[0mweights=['yolov5s-seg.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/predict-seg, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1, retina_masks=False\n",
|
||||
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"\n",
|
||||
"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt to yolov5s-seg.pt...\n",
|
||||
"100% 14.9M/14.9M [00:01<00:00, 12.0MB/s]\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
|
||||
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.2ms\n",
|
||||
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.4ms\n",
|
||||
"Speed: 0.5ms pre-process, 15.8ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640)\n",
|
||||
"Results saved to \u001b[1mruns/predict-seg/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!python segment/predict.py --weights yolov5s-seg.pt --img 640 --conf 0.25 --source data/images\n",
|
||||
"# display.Image(filename='runs/predict-seg/exp/zidane.jpg', width=600)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "hkAzDWJ7cWTr"
|
||||
},
|
||||
"source": [
|
||||
" \n",
|
||||
"<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/199030123-08c72f8d-6871-4116-8ed3-c373642cf28e.jpg\" width=\"600\">"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "0eq1SMWl6Sfn"
|
||||
},
|
||||
"source": [
|
||||
"# 2. Validate\n",
|
||||
"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "WQPtK1QYVaD_",
|
||||
"outputId": "9d751d8c-bee8-4339-cf30-9854ca530449"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017labels-segments.zip ...\n",
|
||||
"Downloading http://images.cocodataset.org/zips/val2017.zip ...\n",
|
||||
"######################################################################## 100.0%\n",
|
||||
"######################################################################## 100.0%\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Download COCO val\n",
|
||||
"!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "X58w8JLpMnjH",
|
||||
"outputId": "a140d67a-02da-479e-9ddb-7d54bf9e407a"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1msegment/val: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s-seg.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val-seg, name=exp, exist_ok=False, half=True, dnn=False\n",
|
||||
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:54<00:00, 1.37it/s]\n",
|
||||
" all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\n",
|
||||
"Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\n",
|
||||
"Results saved to \u001b[1mruns/val-seg/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Validate YOLOv5s-seg on COCO val\n",
|
||||
"!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ZY2VXXXu74w5"
|
||||
},
|
||||
"source": [
|
||||
"# 3. Train\n",
|
||||
"\n",
|
||||
"<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/></a></p>\n",
|
||||
"Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
|
||||
"<br><br>\n",
|
||||
"\n",
|
||||
"Train a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\n",
|
||||
"\n",
|
||||
"- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n",
|
||||
"automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n",
|
||||
"- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n",
|
||||
"- **Training Results** are saved to `runs/train-seg/` with incrementing run directories, i.e. `runs/train-seg/exp2`, `runs/train-seg/exp3` etc.\n",
|
||||
"<br><br>\n",
|
||||
"\n",
|
||||
"A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
|
||||
"\n",
|
||||
"## Train on Custom Data with Roboflow 🌟 NEW\n",
|
||||
"\n",
|
||||
"[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n",
|
||||
"\n",
|
||||
"- Custom Training Example: [https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/](https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/?ref=ultralytics)\n",
|
||||
"- Custom Training Notebook: [](https://colab.research.google.com/drive/1JTz7kpmHsg-5qwVz2d2IH3AaenI1tv0N?usp=sharing)\n",
|
||||
"<br>\n",
|
||||
"\n",
|
||||
"<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"480\" src=\"https://robflow-public-assets.s3.amazonaws.com/how-to-train-yolov5-segmentation-annotation.gif\"/></a></p>Label images lightning fast (including with model-assisted labeling)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "i3oKtE4g-aNn"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title Select YOLOv5 🚀 logger {run: 'auto'}\n",
|
||||
"logger = \"Comet\" # @param ['Comet', 'ClearML', 'TensorBoard']\n",
|
||||
"\n",
|
||||
"if logger == \"Comet\":\n",
|
||||
" %pip install -q comet_ml\n",
|
||||
" import comet_ml\n",
|
||||
"\n",
|
||||
" comet_ml.init()\n",
|
||||
"elif logger == \"ClearML\":\n",
|
||||
" %pip install -q clearml\n",
|
||||
" import clearml\n",
|
||||
"\n",
|
||||
" clearml.browser_login()\n",
|
||||
"elif logger == \"TensorBoard\":\n",
|
||||
" %load_ext tensorboard\n",
|
||||
" %tensorboard --logdir runs/train"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "1NcFxRcFdJ_O",
|
||||
"outputId": "3a3e0cf7-e79c-47a5-c8e7-2d26eeeab988"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1msegment/train: \u001b[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False\n",
|
||||
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
|
||||
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
|
||||
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n",
|
||||
"\n",
|
||||
"Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']\n",
|
||||
"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip to coco128-seg.zip...\n",
|
||||
"100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]\n",
|
||||
"Dataset download success ✅ (1.9s), saved to \u001b[1m/content/datasets\u001b[0m\n",
|
||||
"\n",
|
||||
" from n params module arguments \n",
|
||||
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
|
||||
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
|
||||
" 2 -1 1 18816 models.common.C3 [64, 64, 1] \n",
|
||||
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
|
||||
" 4 -1 2 115712 models.common.C3 [128, 128, 2] \n",
|
||||
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
|
||||
" 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
|
||||
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
|
||||
" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
|
||||
" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n",
|
||||
" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
|
||||
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||||
" 12 [-1, 6] 1 0 models.common.Concat [1] \n",
|
||||
" 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n",
|
||||
" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
|
||||
" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||||
" 16 [-1, 4] 1 0 models.common.Concat [1] \n",
|
||||
" 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n",
|
||||
" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
|
||||
" 19 [-1, 14] 1 0 models.common.Concat [1] \n",
|
||||
" 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n",
|
||||
" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
|
||||
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
|
||||
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
|
||||
" 24 [17, 20, 23] 1 615133 models.yolo.Segment [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], 32, 128, [128, 256, 512]]\n",
|
||||
"Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs\n",
|
||||
"\n",
|
||||
"Transferred 367/367 items from yolov5s-seg.pt\n",
|
||||
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
|
||||
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 bias\n",
|
||||
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1389.59it/s]\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 98.90it/s]\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
|
||||
"Plotting labels to runs/train-seg/exp/labels.jpg... \n",
|
||||
"Image sizes 640 train, 640 val\n",
|
||||
"Using 2 dataloader workers\n",
|
||||
"Logging results to \u001b[1mruns/train-seg/exp\u001b[0m\n",
|
||||
"Starting training for 3 epochs...\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
|
||||
" 0/2 4.92G 0.0417 0.04646 0.06066 0.02126 192 640: 100% 8/8 [00:08<00:00, 1.10s/it]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.81it/s]\n",
|
||||
" all 128 929 0.737 0.649 0.715 0.492 0.719 0.617 0.658 0.408\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
|
||||
" 1/2 6.29G 0.04157 0.04503 0.05772 0.01777 208 640: 100% 8/8 [00:09<00:00, 1.21s/it]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.87it/s]\n",
|
||||
" all 128 929 0.756 0.674 0.738 0.506 0.725 0.64 0.68 0.422\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
|
||||
" 2/2 6.29G 0.0425 0.04793 0.06784 0.01863 161 640: 100% 8/8 [00:03<00:00, 2.02it/s]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.88it/s]\n",
|
||||
" all 128 929 0.736 0.694 0.747 0.522 0.769 0.622 0.683 0.427\n",
|
||||
"\n",
|
||||
"3 epochs completed in 0.009 hours.\n",
|
||||
"Optimizer stripped from runs/train-seg/exp/weights/last.pt, 15.6MB\n",
|
||||
"Optimizer stripped from runs/train-seg/exp/weights/best.pt, 15.6MB\n",
|
||||
"\n",
|
||||
"Validating runs/train-seg/exp/weights/best.pt...\n",
|
||||
"Fusing layers... \n",
|
||||
"Model summary: 165 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:06<00:00, 1.59s/it]\n",
|
||||
" all 128 929 0.738 0.694 0.746 0.522 0.759 0.625 0.682 0.426\n",
|
||||
" person 128 254 0.845 0.756 0.836 0.55 0.861 0.669 0.759 0.407\n",
|
||||
" bicycle 128 6 0.475 0.333 0.549 0.341 0.711 0.333 0.526 0.322\n",
|
||||
" car 128 46 0.612 0.565 0.539 0.257 0.555 0.435 0.477 0.171\n",
|
||||
" motorcycle 128 5 0.73 0.8 0.752 0.571 0.747 0.8 0.752 0.42\n",
|
||||
" airplane 128 6 1 0.943 0.995 0.732 0.92 0.833 0.839 0.555\n",
|
||||
" bus 128 7 0.677 0.714 0.722 0.653 0.711 0.714 0.722 0.593\n",
|
||||
" train 128 3 1 0.951 0.995 0.551 1 0.884 0.995 0.781\n",
|
||||
" truck 128 12 0.555 0.417 0.457 0.285 0.624 0.417 0.397 0.277\n",
|
||||
" boat 128 6 0.624 0.5 0.584 0.186 1 0.326 0.412 0.133\n",
|
||||
" traffic light 128 14 0.513 0.302 0.411 0.247 0.435 0.214 0.376 0.251\n",
|
||||
" stop sign 128 2 0.824 1 0.995 0.796 0.906 1 0.995 0.747\n",
|
||||
" bench 128 9 0.75 0.667 0.763 0.367 0.724 0.585 0.698 0.209\n",
|
||||
" bird 128 16 0.961 1 0.995 0.686 0.918 0.938 0.91 0.525\n",
|
||||
" cat 128 4 0.771 0.857 0.945 0.752 0.76 0.8 0.945 0.728\n",
|
||||
" dog 128 9 0.987 0.778 0.963 0.681 1 0.705 0.89 0.574\n",
|
||||
" horse 128 2 0.703 1 0.995 0.697 0.759 1 0.995 0.249\n",
|
||||
" elephant 128 17 0.916 0.882 0.93 0.691 0.811 0.765 0.829 0.537\n",
|
||||
" bear 128 1 0.664 1 0.995 0.995 0.701 1 0.995 0.895\n",
|
||||
" zebra 128 4 0.864 1 0.995 0.921 0.879 1 0.995 0.804\n",
|
||||
" giraffe 128 9 0.883 0.889 0.94 0.683 0.845 0.778 0.78 0.463\n",
|
||||
" backpack 128 6 1 0.59 0.701 0.372 1 0.474 0.52 0.252\n",
|
||||
" umbrella 128 18 0.654 0.839 0.887 0.52 0.517 0.556 0.427 0.229\n",
|
||||
" handbag 128 19 0.54 0.211 0.408 0.221 0.796 0.206 0.396 0.196\n",
|
||||
" tie 128 7 0.864 0.857 0.857 0.577 0.925 0.857 0.857 0.534\n",
|
||||
" suitcase 128 4 0.716 1 0.945 0.647 0.767 1 0.945 0.634\n",
|
||||
" frisbee 128 5 0.708 0.8 0.761 0.643 0.737 0.8 0.761 0.501\n",
|
||||
" skis 128 1 0.691 1 0.995 0.796 0.761 1 0.995 0.199\n",
|
||||
" snowboard 128 7 0.918 0.857 0.904 0.604 0.32 0.286 0.235 0.137\n",
|
||||
" sports ball 128 6 0.902 0.667 0.701 0.466 0.727 0.5 0.497 0.471\n",
|
||||
" kite 128 10 0.586 0.4 0.511 0.231 0.663 0.394 0.417 0.139\n",
|
||||
" baseball bat 128 4 0.359 0.5 0.401 0.169 0.631 0.5 0.526 0.133\n",
|
||||
" baseball glove 128 7 1 0.519 0.58 0.327 0.687 0.286 0.455 0.328\n",
|
||||
" skateboard 128 5 0.729 0.8 0.862 0.631 0.599 0.6 0.604 0.379\n",
|
||||
" tennis racket 128 7 0.57 0.714 0.645 0.448 0.608 0.714 0.645 0.412\n",
|
||||
" bottle 128 18 0.469 0.393 0.537 0.357 0.661 0.389 0.543 0.349\n",
|
||||
" wine glass 128 16 0.677 0.938 0.866 0.441 0.53 0.625 0.67 0.334\n",
|
||||
" cup 128 36 0.777 0.722 0.812 0.466 0.725 0.583 0.762 0.467\n",
|
||||
" fork 128 6 0.948 0.333 0.425 0.27 0.527 0.167 0.18 0.102\n",
|
||||
" knife 128 16 0.757 0.587 0.669 0.458 0.79 0.5 0.552 0.34\n",
|
||||
" spoon 128 22 0.74 0.364 0.559 0.269 0.925 0.364 0.513 0.213\n",
|
||||
" bowl 128 28 0.766 0.714 0.725 0.559 0.803 0.584 0.665 0.353\n",
|
||||
" banana 128 1 0.408 1 0.995 0.398 0.539 1 0.995 0.497\n",
|
||||
" sandwich 128 2 1 0 0.695 0.536 1 0 0.498 0.448\n",
|
||||
" orange 128 4 0.467 1 0.995 0.693 0.518 1 0.995 0.663\n",
|
||||
" broccoli 128 11 0.462 0.455 0.383 0.259 0.548 0.455 0.384 0.256\n",
|
||||
" carrot 128 24 0.631 0.875 0.77 0.533 0.757 0.909 0.853 0.499\n",
|
||||
" hot dog 128 2 0.555 1 0.995 0.995 0.578 1 0.995 0.796\n",
|
||||
" pizza 128 5 0.89 0.8 0.962 0.796 1 0.778 0.962 0.766\n",
|
||||
" donut 128 14 0.695 1 0.893 0.772 0.704 1 0.893 0.696\n",
|
||||
" cake 128 4 0.826 1 0.995 0.92 0.862 1 0.995 0.846\n",
|
||||
" chair 128 35 0.53 0.571 0.613 0.336 0.67 0.6 0.538 0.271\n",
|
||||
" couch 128 6 0.972 0.667 0.833 0.627 1 0.62 0.696 0.394\n",
|
||||
" potted plant 128 14 0.7 0.857 0.883 0.552 0.836 0.857 0.883 0.473\n",
|
||||
" bed 128 3 0.979 0.667 0.83 0.366 1 0 0.83 0.373\n",
|
||||
" dining table 128 13 0.775 0.308 0.505 0.364 0.644 0.231 0.25 0.0804\n",
|
||||
" toilet 128 2 0.836 1 0.995 0.846 0.887 1 0.995 0.797\n",
|
||||
" tv 128 2 0.6 1 0.995 0.846 0.655 1 0.995 0.896\n",
|
||||
" laptop 128 3 0.822 0.333 0.445 0.307 1 0 0.392 0.12\n",
|
||||
" mouse 128 2 1 0 0 0 1 0 0 0\n",
|
||||
" remote 128 8 0.745 0.5 0.62 0.459 0.821 0.5 0.624 0.449\n",
|
||||
" cell phone 128 8 0.686 0.375 0.502 0.272 0.488 0.25 0.28 0.132\n",
|
||||
" microwave 128 3 0.831 1 0.995 0.722 0.867 1 0.995 0.592\n",
|
||||
" oven 128 5 0.439 0.4 0.435 0.294 0.823 0.6 0.645 0.418\n",
|
||||
" sink 128 6 0.677 0.5 0.565 0.448 0.722 0.5 0.46 0.362\n",
|
||||
" refrigerator 128 5 0.533 0.8 0.783 0.524 0.558 0.8 0.783 0.527\n",
|
||||
" book 128 29 0.732 0.379 0.423 0.196 0.69 0.207 0.38 0.131\n",
|
||||
" clock 128 9 0.889 0.778 0.917 0.677 0.908 0.778 0.875 0.604\n",
|
||||
" vase 128 2 0.375 1 0.995 0.995 0.455 1 0.995 0.796\n",
|
||||
" scissors 128 1 1 0 0.0166 0.00166 1 0 0 0\n",
|
||||
" teddy bear 128 21 0.813 0.829 0.841 0.457 0.826 0.678 0.786 0.422\n",
|
||||
" toothbrush 128 5 0.806 1 0.995 0.733 0.991 1 0.995 0.628\n",
|
||||
"Results saved to \u001b[1mruns/train-seg/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Train YOLOv5s on COCO128 for 3 epochs\n",
|
||||
"!python segment/train.py --img 640 --batch 16 --epochs 3 --data coco128-seg.yaml --weights yolov5s-seg.pt --cache"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "15glLzbQx5u0"
|
||||
},
|
||||
"source": [
|
||||
"# 4. Visualize"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "nWOsI5wJR1o3"
|
||||
},
|
||||
"source": [
|
||||
"## Comet Logging and Visualization 🌟 NEW\n",
|
||||
"\n",
|
||||
"[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!\n",
|
||||
"\n",
|
||||
"Getting started is easy:\n",
|
||||
"```shell\n",
|
||||
"pip install comet_ml # 1. install\n",
|
||||
"export COMET_API_KEY=<Your API Key> # 2. paste API key\n",
|
||||
"python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n",
|
||||
"```\n",
|
||||
"To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n",
|
||||
"[](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n",
|
||||
"\n",
|
||||
"<a href=\"https://bit.ly/yolov5-readme-comet2\">\n",
|
||||
"<img alt=\"Comet Dashboard\" src=\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\" width=\"1280\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Lay2WsTjNJzP"
|
||||
},
|
||||
"source": [
|
||||
"## ClearML Logging and Automation 🌟 NEW\n",
|
||||
"\n",
|
||||
"[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
|
||||
"\n",
|
||||
"- `pip install clearml`\n",
|
||||
"- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
|
||||
"\n",
|
||||
"You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
|
||||
"\n",
|
||||
"You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n",
|
||||
"\n",
|
||||
"<a href=\"https://cutt.ly/yolov5-notebook-clearml\">\n",
|
||||
"<img alt=\"ClearML Experiment Management UI\" src=\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\" width=\"1280\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "-WPvRbS5Swl6"
|
||||
},
|
||||
"source": [
|
||||
"## Local Logging\n",
|
||||
"\n",
|
||||
"Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
|
||||
"\n",
|
||||
"This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
|
||||
"\n",
|
||||
"<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Zelyeqbyt3GD"
|
||||
},
|
||||
"source": [
|
||||
"# Environments\n",
|
||||
"\n",
|
||||
"YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
|
||||
"\n",
|
||||
"- **Notebooks** with free GPU: <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>\n",
|
||||
"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
|
||||
"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
|
||||
"- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <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>\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "6Qu7Iesl0p54"
|
||||
},
|
||||
"source": [
|
||||
"# Status\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "IEijrePND_2I"
|
||||
},
|
||||
"source": [
|
||||
"# Appendix\n",
|
||||
"\n",
|
||||
"Additional content below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "GMusP4OAxFu6"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
|
||||
"\n",
|
||||
"model = torch.hub.load(\n",
|
||||
" \"ultralytics/yolov5\", \"yolov5s-seg\", force_reload=True, trust_repo=True\n",
|
||||
") # or yolov5n - yolov5x6 or custom\n",
|
||||
"im = \"https://ultralytics.com/images/zidane.jpg\" # file, Path, PIL.Image, OpenCV, nparray, list\n",
|
||||
"results = model(im) # inference\n",
|
||||
"results.print() # or .show(), .save(), .crop(), .pandas(), etc."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"name": "YOLOv5 Segmentation Tutorial",
|
||||
"provenance": [],
|
||||
"toc_visible": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
522
yolov5/segment/val.py
Normal file
522
yolov5/segment/val.py
Normal file
@ -0,0 +1,522 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Validate a trained YOLOv5 segment model on a segment dataset.
|
||||
|
||||
Usage:
|
||||
$ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images)
|
||||
$ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments
|
||||
|
||||
Usage - formats:
|
||||
$ python segment/val.py --weights yolov5s-seg.pt # PyTorch
|
||||
yolov5s-seg.torchscript # TorchScript
|
||||
yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s-seg_openvino_label # OpenVINO
|
||||
yolov5s-seg.engine # TensorRT
|
||||
yolov5s-seg.mlmodel # CoreML (macOS-only)
|
||||
yolov5s-seg_saved_model # TensorFlow SavedModel
|
||||
yolov5s-seg.pb # TensorFlow GraphDef
|
||||
yolov5s-seg.tflite # TensorFlow Lite
|
||||
yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s-seg_paddle_model # PaddlePaddle
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
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
|
||||
|
||||
import torch.nn.functional as F
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from models.yolo import SegmentationModel
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
NUM_THREADS,
|
||||
TQDM_BAR_FORMAT,
|
||||
Profile,
|
||||
check_dataset,
|
||||
check_img_size,
|
||||
check_requirements,
|
||||
check_yaml,
|
||||
coco80_to_coco91_class,
|
||||
colorstr,
|
||||
increment_path,
|
||||
non_max_suppression,
|
||||
print_args,
|
||||
scale_boxes,
|
||||
xywh2xyxy,
|
||||
xyxy2xywh,
|
||||
)
|
||||
from utils.metrics import ConfusionMatrix, box_iou
|
||||
from utils.plots import output_to_target, plot_val_study
|
||||
from utils.segment.dataloaders import create_dataloader
|
||||
from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image
|
||||
from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
|
||||
from utils.segment.plots import plot_images_and_masks
|
||||
from utils.torch_utils import de_parallel, select_device, smart_inference_mode
|
||||
|
||||
|
||||
def save_one_txt(predn, save_conf, shape, file):
|
||||
"""Saves detection results in txt format; includes class, xywh (normalized), optionally confidence if `save_conf` is
|
||||
True.
|
||||
"""
|
||||
gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||
for *xyxy, conf, cls in predn.tolist():
|
||||
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||||
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
|
||||
with open(file, "a") as f:
|
||||
f.write(("%g " * len(line)).rstrip() % line + "\n")
|
||||
|
||||
|
||||
def save_one_json(predn, jdict, path, class_map, pred_masks):
|
||||
"""
|
||||
Saves a JSON file with detection results including bounding boxes, category IDs, scores, and segmentation masks.
|
||||
|
||||
Example JSON result: {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}.
|
||||
"""
|
||||
from pycocotools.mask import encode
|
||||
|
||||
def single_encode(x):
|
||||
"""Encodes binary mask arrays into RLE (Run-Length Encoding) format for JSON serialization."""
|
||||
rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
|
||||
rle["counts"] = rle["counts"].decode("utf-8")
|
||||
return rle
|
||||
|
||||
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
|
||||
box = xyxy2xywh(predn[:, :4]) # xywh
|
||||
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
|
||||
pred_masks = np.transpose(pred_masks, (2, 0, 1))
|
||||
with ThreadPool(NUM_THREADS) as pool:
|
||||
rles = pool.map(single_encode, pred_masks)
|
||||
for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())):
|
||||
jdict.append(
|
||||
{
|
||||
"image_id": image_id,
|
||||
"category_id": class_map[int(p[5])],
|
||||
"bbox": [round(x, 3) for x in b],
|
||||
"score": round(p[4], 5),
|
||||
"segmentation": rles[i],
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):
|
||||
"""
|
||||
Return correct prediction matrix
|
||||
Arguments:
|
||||
detections (array[N, 6]), x1, y1, x2, y2, conf, class
|
||||
labels (array[M, 5]), class, x1, y1, x2, y2
|
||||
Returns:
|
||||
correct (array[N, 10]), for 10 IoU levels.
|
||||
"""
|
||||
if masks:
|
||||
if overlap:
|
||||
nl = len(labels)
|
||||
index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
|
||||
gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
|
||||
gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
|
||||
if gt_masks.shape[1:] != pred_masks.shape[1:]:
|
||||
gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
|
||||
gt_masks = gt_masks.gt_(0.5)
|
||||
iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
|
||||
else: # boxes
|
||||
iou = box_iou(labels[:, 1:], detections[:, :4])
|
||||
|
||||
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
|
||||
correct_class = labels[:, 0:1] == detections[:, 5]
|
||||
for i in range(len(iouv)):
|
||||
x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
|
||||
if x[0].shape[0]:
|
||||
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
|
||||
if x[0].shape[0] > 1:
|
||||
matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
|
||||
# matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
|
||||
correct[matches[:, 1].astype(int), i] = True
|
||||
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
data,
|
||||
weights=None, # model.pt path(s)
|
||||
batch_size=32, # batch size
|
||||
imgsz=640, # inference size (pixels)
|
||||
conf_thres=0.001, # confidence threshold
|
||||
iou_thres=0.6, # NMS IoU threshold
|
||||
max_det=300, # maximum detections per image
|
||||
task="val", # train, val, test, speed or study
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
workers=8, # max dataloader workers (per RANK in DDP mode)
|
||||
single_cls=False, # treat as single-class dataset
|
||||
augment=False, # augmented inference
|
||||
verbose=False, # verbose output
|
||||
save_txt=False, # save results to *.txt
|
||||
save_hybrid=False, # save label+prediction hybrid results to *.txt
|
||||
save_conf=False, # save confidences in --save-txt labels
|
||||
save_json=False, # save a COCO-JSON results file
|
||||
project=ROOT / "runs/val-seg", # save to project/name
|
||||
name="exp", # save to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
half=True, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
model=None,
|
||||
dataloader=None,
|
||||
save_dir=Path(""),
|
||||
plots=True,
|
||||
overlap=False,
|
||||
mask_downsample_ratio=1,
|
||||
compute_loss=None,
|
||||
callbacks=Callbacks(),
|
||||
):
|
||||
"""Validates a YOLOv5 segmentation model on specified dataset, producing metrics, plots, and optional JSON
|
||||
output.
|
||||
"""
|
||||
if save_json:
|
||||
check_requirements("pycocotools>=2.0.6")
|
||||
process = process_mask_native # more accurate
|
||||
else:
|
||||
process = process_mask # faster
|
||||
|
||||
# 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()
|
||||
nm = de_parallel(model).model[-1].nm # number of masks
|
||||
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 / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, 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
|
||||
nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks
|
||||
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")
|
||||
|
||||
# Data
|
||||
data = check_dataset(data) # check
|
||||
|
||||
# Configure
|
||||
model.eval()
|
||||
cuda = device.type != "cpu"
|
||||
is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset
|
||||
nc = 1 if single_cls else int(data["nc"]) # number of classes
|
||||
iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
|
||||
niou = iouv.numel()
|
||||
|
||||
# Dataloader
|
||||
if not training:
|
||||
if pt and not single_cls: # check --weights are trained on --data
|
||||
ncm = model.model.nc
|
||||
assert ncm == nc, (
|
||||
f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} "
|
||||
f"classes). Pass correct combination of --weights and --data that are trained together."
|
||||
)
|
||||
model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
|
||||
pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks
|
||||
task = task if task in ("train", "val", "test") else "val" # path to train/val/test images
|
||||
dataloader = create_dataloader(
|
||||
data[task],
|
||||
imgsz,
|
||||
batch_size,
|
||||
stride,
|
||||
single_cls,
|
||||
pad=pad,
|
||||
rect=rect,
|
||||
workers=workers,
|
||||
prefix=colorstr(f"{task}: "),
|
||||
overlap_mask=overlap,
|
||||
mask_downsample_ratio=mask_downsample_ratio,
|
||||
)[0]
|
||||
|
||||
seen = 0
|
||||
confusion_matrix = ConfusionMatrix(nc=nc)
|
||||
names = model.names if hasattr(model, "names") else model.module.names # get class names
|
||||
if isinstance(names, (list, tuple)): # old format
|
||||
names = dict(enumerate(names))
|
||||
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
|
||||
s = ("%22s" + "%11s" * 10) % (
|
||||
"Class",
|
||||
"Images",
|
||||
"Instances",
|
||||
"Box(P",
|
||||
"R",
|
||||
"mAP50",
|
||||
"mAP50-95)",
|
||||
"Mask(P",
|
||||
"R",
|
||||
"mAP50",
|
||||
"mAP50-95)",
|
||||
)
|
||||
dt = Profile(device=device), Profile(device=device), Profile(device=device)
|
||||
metrics = Metrics()
|
||||
loss = torch.zeros(4, device=device)
|
||||
jdict, stats = [], []
|
||||
# callbacks.run('on_val_start')
|
||||
pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
|
||||
for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar):
|
||||
# callbacks.run('on_val_batch_start')
|
||||
with dt[0]:
|
||||
if cuda:
|
||||
im = im.to(device, non_blocking=True)
|
||||
targets = targets.to(device)
|
||||
masks = masks.to(device)
|
||||
masks = masks.float()
|
||||
im = im.half() if half else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
nb, _, height, width = im.shape # batch size, channels, height, width
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None)
|
||||
|
||||
# Loss
|
||||
if compute_loss:
|
||||
loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls
|
||||
|
||||
# NMS
|
||||
targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
|
||||
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
|
||||
with dt[2]:
|
||||
preds = non_max_suppression(
|
||||
preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det, nm=nm
|
||||
)
|
||||
|
||||
# Metrics
|
||||
plot_masks = [] # masks for plotting
|
||||
for si, (pred, proto) in enumerate(zip(preds, protos)):
|
||||
labels = targets[targets[:, 0] == si, 1:]
|
||||
nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
|
||||
path, shape = Path(paths[si]), shapes[si][0]
|
||||
correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
|
||||
correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
|
||||
seen += 1
|
||||
|
||||
if npr == 0:
|
||||
if nl:
|
||||
stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0]))
|
||||
if plots:
|
||||
confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
|
||||
continue
|
||||
|
||||
# Masks
|
||||
midx = [si] if overlap else targets[:, 0] == si
|
||||
gt_masks = masks[midx]
|
||||
pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:])
|
||||
|
||||
# Predictions
|
||||
if single_cls:
|
||||
pred[:, 5] = 0
|
||||
predn = pred.clone()
|
||||
scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
|
||||
|
||||
# Evaluate
|
||||
if nl:
|
||||
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
|
||||
scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
|
||||
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
|
||||
correct_bboxes = process_batch(predn, labelsn, iouv)
|
||||
correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)
|
||||
if plots:
|
||||
confusion_matrix.process_batch(predn, labelsn)
|
||||
stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)
|
||||
|
||||
pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
|
||||
if plots and batch_i < 3:
|
||||
plot_masks.append(pred_masks[:15]) # filter top 15 to plot
|
||||
|
||||
# Save/log
|
||||
if save_txt:
|
||||
save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt")
|
||||
if save_json:
|
||||
pred_masks = scale_image(
|
||||
im[si].shape[1:], pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1]
|
||||
)
|
||||
save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary
|
||||
# callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
|
||||
|
||||
# Plot images
|
||||
if plots and batch_i < 3:
|
||||
if len(plot_masks):
|
||||
plot_masks = torch.cat(plot_masks, dim=0)
|
||||
plot_images_and_masks(im, targets, masks, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names)
|
||||
plot_images_and_masks(
|
||||
im,
|
||||
output_to_target(preds, max_det=15),
|
||||
plot_masks,
|
||||
paths,
|
||||
save_dir / f"val_batch{batch_i}_pred.jpg",
|
||||
names,
|
||||
) # pred
|
||||
|
||||
# callbacks.run('on_val_batch_end')
|
||||
|
||||
# Compute metrics
|
||||
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
|
||||
if len(stats) and stats[0].any():
|
||||
results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names)
|
||||
metrics.update(results)
|
||||
nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class
|
||||
|
||||
# Print results
|
||||
pf = "%22s" + "%11i" * 2 + "%11.3g" * 8 # print format
|
||||
LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results()))
|
||||
if nt.sum() == 0:
|
||||
LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels")
|
||||
|
||||
# Print results per class
|
||||
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
|
||||
for i, c in enumerate(metrics.ap_class_index):
|
||||
LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i)))
|
||||
|
||||
# Print speeds
|
||||
t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
|
||||
if not training:
|
||||
shape = (batch_size, 3, imgsz, imgsz)
|
||||
LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t)
|
||||
|
||||
# Plots
|
||||
if plots:
|
||||
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
|
||||
# callbacks.run('on_val_end')
|
||||
|
||||
mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results()
|
||||
|
||||
# Save JSON
|
||||
if save_json and len(jdict):
|
||||
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights
|
||||
anno_json = str(Path("../../datasets/coco/annotations/instances_val2017.json")) # annotations
|
||||
pred_json = str(save_dir / f"{w}_predictions.json") # predictions
|
||||
LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...")
|
||||
with open(pred_json, "w") as f:
|
||||
json.dump(jdict, f)
|
||||
|
||||
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||||
from pycocotools.coco import COCO
|
||||
from pycocotools.cocoeval import COCOeval
|
||||
|
||||
anno = COCO(anno_json) # init annotations api
|
||||
pred = anno.loadRes(pred_json) # init predictions api
|
||||
results = []
|
||||
for eval in COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "segm"):
|
||||
if is_coco:
|
||||
eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate
|
||||
eval.evaluate()
|
||||
eval.accumulate()
|
||||
eval.summarize()
|
||||
results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5)
|
||||
map_bbox, map50_bbox, map_mask, map50_mask = results
|
||||
except Exception as e:
|
||||
LOGGER.info(f"pycocotools unable to run: {e}")
|
||||
|
||||
# Return results
|
||||
model.float() # for training
|
||||
if not training:
|
||||
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}")
|
||||
final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask
|
||||
return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""Parses command line arguments for configuring YOLOv5 options like dataset path, weights, batch size, and
|
||||
inference settings.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path")
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)")
|
||||
parser.add_argument("--batch-size", type=int, default=32, help="batch size")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
|
||||
parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold")
|
||||
parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold")
|
||||
parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image")
|
||||
parser.add_argument("--task", default="val", help="train, val, test, speed or study")
|
||||
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("--single-cls", action="store_true", help="treat as single-class dataset")
|
||||
parser.add_argument("--augment", action="store_true", help="augmented inference")
|
||||
parser.add_argument("--verbose", action="store_true", help="report mAP by class")
|
||||
parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
|
||||
parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt")
|
||||
parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
|
||||
parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file")
|
||||
parser.add_argument("--project", default=ROOT / "runs/val-seg", help="save results 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()
|
||||
opt.data = check_yaml(opt.data) # check YAML
|
||||
# opt.save_json |= opt.data.endswith('coco.yaml')
|
||||
opt.save_txt |= opt.save_hybrid
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes YOLOv5 tasks including training, validation, testing, speed, and study with configurable options."""
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
|
||||
if opt.task in ("train", "val", "test"): # run normally
|
||||
if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
|
||||
LOGGER.warning(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results")
|
||||
if opt.save_hybrid:
|
||||
LOGGER.warning("WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone")
|
||||
run(**vars(opt))
|
||||
|
||||
else:
|
||||
weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
|
||||
opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results
|
||||
if opt.task == "speed": # speed benchmarks
|
||||
# python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
|
||||
opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
|
||||
for opt.weights in weights:
|
||||
run(**vars(opt), plots=False)
|
||||
|
||||
elif opt.task == "study": # speed vs mAP benchmarks
|
||||
# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
|
||||
for opt.weights in weights:
|
||||
f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to
|
||||
x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
|
||||
for opt.imgsz in x: # img-size
|
||||
LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...")
|
||||
r, _, t = run(**vars(opt), plots=False)
|
||||
y.append(r + t) # results and times
|
||||
np.savetxt(f, y, fmt="%10.4g") # save
|
||||
subprocess.run(["zip", "-r", "study.zip", "study_*.txt"])
|
||||
plot_val_study(x=x) # plot
|
||||
else:
|
||||
raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
Reference in New Issue
Block a user