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xiang-wuu/yolov5

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English | 简体中文 <br> <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="CI CPU testing"></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> <br> <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/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> <a href="https://join.slack.com/t/ultralytics/sharedinvite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-JoinForum-blue.svg?logo=slack" alt="Join Forum"></a>

<br> <p> YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://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. </p>

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<div align="center">Documentation</div>

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

<div align="center">Quick Start Examples</div>

<details open> <summary>Install</summary>

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

bash

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

</details>

<details open> <summary>Inference</summary>

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

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 automatically from the latest YOLOv5 release and saving results to runs/detect.

bash

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

</details>

<details> <summary>Training</summary>

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.

bash

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

</details>

<details open> <summary>Tutorials</summary>

—Train Custom Data 🚀 RECOMMENDED
—Tips for Best Training Results ☘️ RECOMMENDED
—Weights & Biases Logging 🌟 NEW
—Roboflow for Datasets, Labeling, and Active Learning 🌟 NEW
—Multi-GPU Training
—PyTorch Hub ⭐ NEW
—TFLite, ONNX, CoreML, TensorRT Export 🚀
—Test-Time Augmentation (TTA)
—Model Ensembling
—Model Pruning/Sparsity
—Hyperparameter Evolution
—Transfer Learning with Frozen Layers ⭐ NEW
—Architecture Summary ⭐ NEW

</details>

<div align="center">Environments</div>

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

<div align="center">Integrations</div>

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Weights and Biases	Roboflow ⭐ NEW	Gradio with Hugging Face Spaces
Automatically track and visualize all your YOLOv5 training runs in the cloud with Weights & Biases	Label and export your custom datasets directly to YOLOv5 for training with Roboflow	With Gradio one can quickly create a user-friendly web app to showcase what a NN model can do and it can be hosted with Hugging Face Spaces 🤗 to make it accessible to the world.

<!-- ## <div align="center">Compete and Win</div>

We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with $10,000 in cash prizes!

<div align="center">Why YOLOv5</div>

<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 (click to expand)</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 (click to expand)</summary>

—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

</details>

Pretrained Checkpoints

Model	size<br><sup>(pixels)	mAP<sup>val<br>0.5:0.95	mAP<sup>val<br>0.5	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][assets]	640	28.0	45.7	45	6.3	0.6	1.9	4.5
[YOLOv5s][assets]	640	37.4	56.8	98	6.4	0.9	7.2	16.5
[YOLOv5m][assets]	640	45.4	64.1	224	8.2	1.7	21.2	49.0
[YOLOv5l][assets]	640	49.0	67.3	430	10.1	2.7	46.5	109.1
[YOLOv5x][assets]	640	50.7	68.9	766	12.1	4.8	86.7	205.7

[YOLOv5n6][assets]	1280	36.0	54.4	153	8.1	2.1	3.2	4.6
[YOLOv5s6][assets]	1280	44.8	63.7	385	8.2	3.6	12.6	16.8
[YOLOv5m6][assets]	1280	51.3	69.3	887	11.1	6.8	35.7	50.0
[YOLOv5l6][assets]	1280	53.7	71.3	1784	15.8	10.5	76.8	111.4
[YOLOv5x6][assets]<br>+ [TTA][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 (click to expand)</summary>

—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.
—mAP<sup>val</sup> values are for single-model single-scale on COCO val2017 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 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 includes reflection and scale augmentations.<br>Reproduce by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

</details>

<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 to get started, and fill out the YOLOv5 Survey to send us feedback on your experiences. Thank you to all our contributors!

<div align="center">Contact</div>

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

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[assets]: https://github.com/ultralytics/yolov5/releases [tta]: https://github.com/ultralytics/yolov5/issues/303