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| <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> | |
| 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> | |
| Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) | |
| . [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 yolov5m, yolov5l, yolov5x, 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 | |
| * [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx) | |
| </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/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png"></p> | |
| <details> | |
| <summary>YOLOv5-P5 640 Figure (click to expand)</summary> | |
| <p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136763877-b174052b-c12f-48d2-8bc4-545e3853398e.png"></p> | |
| </details> | |
| <details> | |
| <summary>Figure Notes (click to expand)</summary> | |
| * **COCO AP val** denotes [email protected]: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 | |
| [assets]: https://github.com/ultralytics/yolov5/releases | |
| [TTA]: https://github.com/ultralytics/yolov5/issues/303 | |
| |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 |28.4 |46.0 |**45** |**6.3**|**0.6**|**1.9**|**4.5** | |
| |[YOLOv5s][assets] |640 |37.2 |56.0 |98 |6.4 |0.9 |7.2 |16.5 | |
| |[YOLOv5m][assets] |640 |45.2 |63.9 |224 |8.2 |1.7 |21.2 |49.0 | |
| |[YOLOv5l][assets] |640 |48.8 |67.2 |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 |34.0 |50.7 |153 |8.1 |2.1 |3.2 |4.6 | |
| |[YOLOv5s6][assets] |1280 |44.5 |63.0 |385 |8.2 |3.6 |12.6 |16.8 | |
| |[YOLOv5m6][assets] |1280 |51.0 |69.0 |887 |11.1 |6.8 |35.7 |50.0 | |
| |[YOLOv5l6][assets] |1280 |53.6 |71.6 |1784 |15.8 |10.5 |76.7 |111.4 | |
| |[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |54.7<br>**55.4** |**72.4**<br>72.3 |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 and hyperparameters. | |
| * **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). | |
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