RODY/app/yolov5
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.github first commit 2022-11-04 17:37:08 +08:00
classify 第一次调试 2022-11-07 14:34:35 +08:00
data 调试 2022-11-08 09:59:54 +08:00
models 第一次调试 2022-11-07 14:34:35 +08:00
utils 修改cache存在报错情况 2022-11-11 10:54:30 +08:00
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CONTRIBUTING.md first commit 2022-11-04 17:37:08 +08:00
detect_server.py 调试 2022-11-08 14:34:00 +08:00
export.py 输出模型imgsz参数添加 2022-11-17 09:52:26 +08:00
hubconf.py 第一次调试 2022-11-07 14:34:35 +08:00
LICENSE first commit 2022-11-04 17:37:08 +08:00
README.md first commit 2022-11-04 17:37:08 +08:00
train_server.py 实时返回训练loss report 2022-11-18 15:20:33 +08:00
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val.py first commit 2022-11-04 17:37:08 +08:00
validate_server.py first commit 2022-11-04 17:37:08 +08:00
yolov5s.pt 调试 2022-11-07 20:51:30 +08:00
zdp_R-ODYv1.pt 第一次调试 2022-11-07 14:38:35 +08:00



 

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CI CPU testing YOLOv5 Citation Docker Pulls
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YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

Documentation

See the YOLOv5 Docs for full documentation on training, testing and deployment.

Quick Start Examples

Install

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install
Inference

YOLOv5 PyTorch Hub inference. Models download automatically from the latest YOLOv5 release.

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.
Inference with detect.py

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

python detect.py --source 0  # webcam
                          img.jpg  # image
                          vid.mp4  # video
                          path/  # directory
                          'path/*.jpg'  # glob
                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
Training

The commands below reproduce YOLOv5 COCO results. Models and datasets download automatically from the latest YOLOv5 release. Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU (Multi-GPU times faster). Use the largest --batch-size possible, or pass --batch-size -1 for YOLOv5 AutoBatch. Batch sizes shown for V100-16GB.

python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
                                       yolov5s                                64
                                       yolov5m                                40
                                       yolov5l                                24
                                       yolov5x                                16
Tutorials

Environments

Get started in seconds with our verified environments. Click each icon below for details.

Integrations

Deci NEW ClearML NEW Roboflow Weights & Biases
Automatically compile and quantize YOLOv5 for better inference performance in one click at Deci Automatically track, visualize and even remotely train YOLOv5 using ClearML (open-source!) Label and export your custom datasets directly to YOLOv5 for training with Roboflow Automatically track and visualize all your YOLOv5 training runs in the cloud with Weights & Biases

Why YOLOv5

YOLOv5-P5 640 Figure (click to expand)

Figure Notes (click to expand)
  • COCO AP val denotes mAP@0.5:0.95 metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536.
  • GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32.
  • EfficientDet data from 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

Pretrained Checkpoints

Model size
(pixels)
mAPval
0.5:0.95
mAPval
0.5
Speed
CPU b1
(ms)
Speed
V100 b1
(ms)
Speed
V100 b32
(ms)
params
(M)
FLOPs
@640 (B)
YOLOv5n 640 28.0 45.7 45 6.3 0.6 1.9 4.5
YOLOv5s 640 37.4 56.8 98 6.4 0.9 7.2 16.5
YOLOv5m 640 45.4 64.1 224 8.2 1.7 21.2 49.0
YOLOv5l 640 49.0 67.3 430 10.1 2.7 46.5 109.1
YOLOv5x 640 50.7 68.9 766 12.1 4.8 86.7 205.7
YOLOv5n6 1280 36.0 54.4 153 8.1 2.1 3.2 4.6
YOLOv5s6 1280 44.8 63.7 385 8.2 3.6 12.6 16.8
YOLOv5m6 1280 51.3 69.3 887 11.1 6.8 35.7 50.0
YOLOv5l6 1280 53.7 71.3 1784 15.8 10.5 76.8 111.4
YOLOv5x6
+ TTA
1280
1536
55.0
55.8
72.7
72.7
3136
-
26.2
-
19.4
-
140.7
-
209.8
-
Table Notes (click to expand)
  • All checkpoints are trained to 300 epochs with default settings. Nano and Small models use hyp.scratch-low.yaml hyps, all others use hyp.scratch-high.yaml.
  • mAPval values are for single-model single-scale on COCO val2017 dataset.
    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 instance. NMS times (~1 ms/img) not included.
    Reproduce by python val.py --data coco.yaml --img 640 --task speed --batch 1
  • TTA Test Time Augmentation includes reflection and scale augmentations.
    Reproduce by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

Classification NEW

YOLOv5 release v6.2 brings support for classification model training, validation, prediction and export! We've made training classifier models super simple. Click below to get started.

Classification Checkpoints (click to expand)

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 for easy reproducibility.

Model size
(pixels)
acc
top1
acc
top5
Training
90 epochs
4xA100 (hours)
Speed
ONNX CPU
(ms)
Speed
TensorRT V100
(ms)
params
(M)
FLOPs
@224 (B)
YOLOv5n-cls 224 64.6 85.4 7:59 3.3 0.5 2.5 0.5
YOLOv5s-cls 224 71.5 90.2 8:09 6.6 0.6 5.4 1.4
YOLOv5m-cls 224 75.9 92.9 10:06 15.5 0.9 12.9 3.9
YOLOv5l-cls 224 78.0 94.0 11:56 26.9 1.4 26.5 8.5
YOLOv5x-cls 224 79.0 94.4 15:04 54.3 1.8 48.1 15.9
ResNet18 224 70.3 89.5 6:47 11.2 0.5 11.7 3.7
ResNet34 224 73.9 91.8 8:33 20.6 0.9 21.8 7.4
ResNet50 224 76.8 93.4 11:10 23.4 1.0 25.6 8.5
ResNet101 224 78.5 94.3 17:10 42.1 1.9 44.5 15.9
EfficientNet_b0 224 75.1 92.4 13:03 12.5 1.3 5.3 1.0
EfficientNet_b1 224 76.4 93.2 17:04 14.9 1.6 7.8 1.5
EfficientNet_b2 224 76.6 93.4 17:10 15.9 1.6 9.1 1.7
EfficientNet_b3 224 77.7 94.0 19:19 18.9 1.9 12.2 2.4
Table Notes (click to expand)
  • 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.
    Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
  • Accuracy values are for single-model single-scale on ImageNet-1k dataset.
    Reproduce by python classify/val.py --data ../datasets/imagenet --img 224
  • Speed averaged over 100 inference images using a Google Colab Pro V100 High-RAM instance.
    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.
    Reproduce by python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224
Classification Usage Examples (click to expand)

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.

# 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 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:

python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
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:

python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224

Contribute

We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our Contributing Guide to get started, and fill out the YOLOv5 Survey to send us feedback on your experiences. Thank you to all our contributors!

Contact

For YOLOv5 bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please visit https://ultralytics.com/contact.