| <div align="center"> | |
| <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> | |
| </p> | |
| <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/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></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> | |
| <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="2%"/> | |
| </a> | |
| <img width="2%" /> | |
| <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%"/> | |
| </a> | |
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| <a href="https://twitter.com/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/> | |
| </a> | |
| <img width="2%" /> | |
| <a href="https://www.producthunt.com/@glenn_jocher"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="2%"/> | |
| </a> | |
| <img width="2%" /> | |
| <a href="https://youtube.com/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/> | |
| </a> | |
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| <a href="https://www.facebook.com/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/> | |
| </a> | |
| <img width="2%" /> | |
| <a href="https://www.instagram.com/ultralytics/"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/> | |
| </a> | |
| </div> | |
| <!-- | |
| <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> | |
| Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a | |
| [**Python>=3.7.0**](https://www.python.org/) environment, including | |
| [**PyTorch>=1.7**](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 open> | |
| <summary>Inference</summary> | |
| YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 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 --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](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://github.com/ultralytics/yolov5/issues/475) 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 --cfg yolov5n.yaml --weights '' --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://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 | |
| - [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW | |
| - [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) | |
| - [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW | |
| - [TFLite, ONNX, CoreML, TensorRT 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 | |
| - [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) ⭐ NEW | |
| </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://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> | |
| </div> | |
| ## <div align="center">Integrations</div> | |
| <div align="center"> | |
| <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width="49%"/> | |
| </a> | |
| <a href="https://roboflow.com/?ref=ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png" width="49%"/> | |
| </a> | |
| </div> | |
| |Weights and Biases|Roboflow ⭐ NEW| | |
| |:-:|:-:| | |
| |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | | |
| <!-- ## <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! | |
| <p align="center"> | |
| <a href="https://github.com/ultralytics/yolov5/discussions/3213"> | |
| <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"></a> | |
| </p> --> | |
| ## <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](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/p3/) 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>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](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/p3/) 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://github.com/ultralytics/yolov5/issues/303) 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](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://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! | |
| <a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a> | |
| ## <div align="center">Contact</div> | |
| For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries 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%" /> | |
| <a href="https://www.linkedin.com/company/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/> | |
| </a> | |
| <img width="3%" /> | |
| <a href="https://twitter.com/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/> | |
| </a> | |
| <img width="3%" /> | |
| <a href="https://www.producthunt.com/@glenn_jocher"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="3%"/> | |
| </a> | |
| <img width="3%" /> | |
| <a href="https://youtube.com/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/> | |
| </a> | |
| <img width="3%" /> | |
| <a href="https://www.facebook.com/ultralytics"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/> | |
| </a> | |
| <img width="3%" /> | |
| <a href="https://www.instagram.com/ultralytics/"> | |
| <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/> | |
| </a> | |
| </div> | |
| [assets]: https://github.com/ultralytics/yolov5/releases | |
| [tta]: https://github.com/ultralytics/yolov5/issues/303 | |