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<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>
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<div>
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<br>
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</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>

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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>

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)&nbsp; 🚀 RECOMMENDED
* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)&nbsp; ☘️
  RECOMMENDED
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)&nbsp; 🌟 NEW
* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)&nbsp; 🌟 NEW
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)&nbsp; ⭐ 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)&nbsp; ⭐ 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).

<br>

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    <a href="https://github.com/ultralytics">
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