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<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>
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</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**](https://www.python.org/) is required with all
[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
```bash
$ git clone https://github.com/ultralytics/yolov5
$ cd 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, 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 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
'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 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
* [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!
<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="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 val.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 val.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 val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half`
* 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 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.
## <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>
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