| <a href="https://apps.apple.com/app/id1452689527" target="_blank"> | |
| <img src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg" width="1000"></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="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/103594689-455e0e00-4eae-11eb-9cdf-7d753e2ceeeb.png"></p> | |
| <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. | |
| </details> | |
| - **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. | |
| - **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, improved speed and mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972). | |
| - **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145). | |
| ## Pretrained Checkpoints | |
| | Model | size | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>V100</sub> | FPS<sub>V100</sub> || params | GFLOPS | | |
| |---------- |------ |------ |------ |------ | -------- | ------| ------ |------ | :------: | | |
| | [YOLOv5s](https://github.com/ultralytics/yolov5/releases) |640 |36.8 |36.8 |55.6 |**2.2ms** |**455** ||7.3M |17.0 | |
| | [YOLOv5m](https://github.com/ultralytics/yolov5/releases) |640 |44.5 |44.5 |63.1 |2.9ms |345 ||21.4M |51.3 | |
| | [YOLOv5l](https://github.com/ultralytics/yolov5/releases) |640 |48.1 |48.1 |66.4 |3.8ms |264 ||47.0M |115.4 | |
| | [YOLOv5x](https://github.com/ultralytics/yolov5/releases) |640 |**50.1** |**50.1** |**68.7** |6.0ms |167 ||87.7M |218.8 | |
| | | | | | | | || | | |
| | [YOLOv5x](https://github.com/ultralytics/yolov5/releases) + TTA |832 |**51.9** |**51.9** |**69.6** |24.9ms |40 ||87.7M |1005.3 | |
| <!--- | |
| | [YOLOv5l6](https://github.com/ultralytics/yolov5/releases) |640 |49.0 |49.0 |67.4 |4.1ms |244 ||77.2M |117.7 | |
| | [YOLOv5l6](https://github.com/ultralytics/yolov5/releases) |1280 |53.0 |53.0 |70.8 |12.3ms |81 ||77.2M |117.7 | |
| ---> | |
| <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 832 --iou 0.65 --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 | |
| rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream | |
| rtmp://192.168.1.105/live/test # rtmp stream | |
| http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # 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 src="https://user-images.githubusercontent.com/26833433/97107365-685a8d80-16c7-11eb-8c2e-83aac701d8b9.jpeg" width="500"> | |
| ### PyTorch Hub | |
| To run **batched inference** with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36): | |
| ```python | |
| import torch | |
| # Model | |
| model = torch.hub.load('ultralytics/yolov5', 'yolov5s') | |
| # Images | |
| dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/' | |
| imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')] # batch of images | |
| # Inference | |
| results = model(imgs) | |
| 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 src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" width="900"> | |
| ## 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. | |