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| <p> | |
| <a align="left" href="https://ultralytics.com/yolov5" target="_blank"> | |
| <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a> | |
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| <a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a> | |
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| <br> | |
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| <a href="https://github.com/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/> | |
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| <a href="https://www.linkedin.com/company/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/> | |
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| <a href="https://youtube.com/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/> | |
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| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/> | |
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| </a> | |
| </div> | |
| <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> | |
| <!-- | |
| <a align="center" href="https://ultralytics.com/yolov5" target="_blank"> | |
| <img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a> | |
| --> | |
| </div> | |
| ## <div align="center">Documentation</div> | |
| See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. | |
| ## <div align="center">Quick Start Examples</div> | |
| <details open> | |
| <summary> | |
| Install | |
| </summary> | |
| Python >= 3.6.0 required with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed: | |
| <!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev --> | |
| ```bash | |
| $ git clone https://github.com/ultralytics/yolov5 | |
| $ pip install -r requirements.txt | |
| ``` | |
| </details> | |
| <details open> | |
| <summary>Inference</summary> | |
| Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). | |
| ```python | |
| import torch | |
| # Model | |
| model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5x, custom | |
| # Images | |
| img = 'https://ultralytics.com/images/zidane.jpg' # or file, PIL, OpenCV, numpy, multiple | |
| # 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](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. | |
| ```bash | |
| $ python detect.py --source 0 # webcam | |
| file.jpg # image | |
| file.mp4 # video | |
| path/ # directory | |
| path/*.jpg # glob | |
| 'https://youtu.be/NUsoVlDFqZg' # YouTube video | |
| 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream | |
| ``` | |
| </details> | |
| <details> | |
| <summary>Training</summary> | |
| Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). | |
| ```bash | |
| $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 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> | |
| <summary>Tutorials</summary> | |
| * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 RECOMMENDED | |
| * [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️ RECOMMENDED | |
| * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW | |
| * [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518) 🌟 NEW | |
| * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) | |
| * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW | |
| * [TorchScript, ONNX, CoreML Export](https://github.com/ultralytics/yolov5/issues/251) 🚀 | |
| * [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) | |
| * [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) | |
| * [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) | |
| * [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) | |
| * [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW | |
| * [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx) | |
| </details> | |
| ## <div align="center">Environments and Integrations</div> | |
| Get started in seconds with our verified environments and integrations, including [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) for automatic YOLOv5 experiment logging. Click each icon below for details. | |
| <div align="center"> | |
| <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/> | |
| </a> | |
| <a href="https://www.kaggle.com/ultralytics/yolov5"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/> | |
| </a> | |
| <a href="https://hub.docker.com/r/ultralytics/yolov5"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/> | |
| </a> | |
| <a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/> | |
| </a> | |
| <a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/> | |
| </a> | |
| <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-small.png" width="15%"/> | |
| </a> | |
| </div> | |
| ## <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"> | |
| <a href="https://github.com/ultralytics/yolov5/discussions/3213"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"/> | |
| </a> | |
| </div> | |
| ## <div align="center">Why YOLOv5</div> | |
| <p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p> | |
| <details> | |
| <summary>YOLOv5-P5 640 Figure (click to expand)</summary> | |
| <p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p> | |
| </details> | |
| <details> | |
| <summary>Figure Notes (click to expand)</summary> | |
| * GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. | |
| * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. | |
| * **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` | |
| </details> | |
| ### Pretrained Checkpoints | |
| [assets]: https://github.com/ultralytics/yolov5/releases | |
| |Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPs<br><sup>640 (B) | |
| |--- |--- |--- |--- |--- |--- |---|--- |--- | |
| |[YOLOv5s][assets] |640 |36.7 |36.7 |55.4 |**2.0** | |7.3 |17.0 | |
| |[YOLOv5m][assets] |640 |44.5 |44.5 |63.1 |2.7 | |21.4 |51.3 | |
| |[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4 | |
| |[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8 | |
| | | | | | | | | | | |
| |[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4 | |
| |[YOLOv5m6][assets] |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4 | |
| |[YOLOv5l6][assets] |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7 | |
| |[YOLOv5x6][assets] |1280 |**54.4** |**54.4** |**72.0** |22.4 | |141.8 |222.9 | |
| | | | | | | | | | | |
| |[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |- |- | |
| <details> | |
| <summary>Table Notes (click to expand)</summary> | |
| * AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. | |
| * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` | |
| * Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` | |
| * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). | |
| * Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python test.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](CONTRIBUTING.md) to get started. | |
| ## <div align="center">Contact</div> | |
| For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or professional support requests please visit | |
| [https://ultralytics.com/contact](https://ultralytics.com/contact). | |
| <br> | |
| <div align="center"> | |
| <a href="https://github.com/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/> | |
| </a> | |
| <img width="3%" /> | |
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| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/> | |
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