| <a align="left" href="https://apps.apple.com/app/id1452689527" target="_blank"> | |
| <img width="800" src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg"></a> | |
|   | |
| <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> | |
| This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk. | |
| <p align="left"><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="left"><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> | |
| - **April 11, 2021**: [v5.0 release](https://github.com/ultralytics/yolov5/releases/tag/v5.0): YOLOv5-P6 1280 models, [AWS](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart), [Supervise.ly](https://github.com/ultralytics/yolov5/issues/2518) and [YouTube](https://github.com/ultralytics/yolov5/pull/2752) integrations. | |
| - **January 5, 2021**: [v4.0 release](https://github.com/ultralytics/yolov5/releases/tag/v4.0): nn.SiLU() activations, [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) logging, [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) integration. | |
| - **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP. | |
| - **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP. | |
| ## 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> | |
| ## Requirements | |
| Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run: | |
| ```bash | |
| $ pip install -r requirements.txt | |
| ``` | |
| ## Tutorials | |
| * [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 | |
| * [ONNX and TorchScript 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) | |
| ## Environments | |
| YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): | |
| - **Google Colab and Kaggle** notebooks with free GPU: <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> | |
| - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) | |
| - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart) | |
| - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <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> | |
| ## Inference | |
| `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 | |
| ``` | |
| To run inference on example images in `data/images`: | |
| ```bash | |
| $ python detect.py --source data/images --weights yolov5s.pt --conf 0.25 | |
| Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt']) | |
| YOLOv5 v4.0-96-g83dc1b4 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB) | |
| Fusing layers... | |
| Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS | |
| image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.010s) | |
| image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.011s) | |
| Results saved to runs/detect/exp2 | |
| Done. (0.103s) | |
| ``` | |
| <img width="500" src="https://user-images.githubusercontent.com/26833433/97107365-685a8d80-16c7-11eb-8c2e-83aac701d8b9.jpeg"> | |
| ### PyTorch Hub | |
| Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36): | |
| ```python | |
| import torch | |
| # Model | |
| model = torch.hub.load('ultralytics/yolov5', 'yolov5s') | |
| # Image | |
| img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' | |
| # Inference | |
| results = model(img) | |
| results.print() # or .show(), .save() | |
| ``` | |
| ## Training | |
| 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"> | |
| ## Citation | |
| [](https://zenodo.org/badge/latestdoi/264818686) | |
| ## About Us | |
| Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including: | |
| - **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.** | |
| - **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.** | |
| - **Custom data training**, hyperparameter evolution, and model exportation to any destination. | |
| For business inquiries and professional support requests please visit us at https://www.ultralytics.com. | |
| ## Contact | |
| **Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com. | |