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< div align = "center" >
< p >
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< 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 >
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< / p >
< br >
< div >
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< 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 >
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< a href = "https://zenodo.org/badge/latestdoi/264818686" > < img src = "https://zenodo.org/badge/264818686.svg" alt = "YOLOv5 Citation" > < / a >
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< br >
< 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 >
< 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 >
< / div >
< br >
< div align = "center" >
< a href = "https://github.com/ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width = "2%" / >
< / a >
< img width = "2%" / >
< a href = "https://www.linkedin.com/company/ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width = "2%" / >
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< a href = "https://twitter.com/ultralytics" >
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< a href = "https://youtube.com/ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width = "2%" / >
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< a href = "https://www.facebook.com/ultralytics" >
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< a href = "https://www.instagram.com/ultralytics/" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width = "2%" / >
< / a >
< / 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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< img width = "800" src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png" > < / a >
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## <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 >
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< summary > Install< / summary >
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[**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/ ):
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<!-- $ sudo apt update && apt install - y libgl1 - mesa - glx libsm6 libxext6 libxrender - dev -->
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```bash
$ git clone https://github.com/ultralytics/yolov5
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$ cd yolov5
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$ pip install -r requirements.txt
```
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< / details >
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< details open >
< summary > Inference< / summary >
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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 ).
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```python
import torch
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# Model
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom
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# Images
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img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
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# Inference
results = model(img)
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# Results
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```
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< / details >
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< details >
< summary > Inference with detect.py< / summary >
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`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` .
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```bash
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$ python detect.py --source 0 # webcam
file.jpg # image
file.mp4 # video
path/ # directory
path/*.jpg # glob
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'https://youtu.be/NUsoVlDFqZg' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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```
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< / details >
< details >
< summary > Training< / summary >
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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).
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```bash
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 40
yolov5l 24
yolov5x 16
```
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< img width = "800" src = "https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" >
< / details >
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< details open >
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< summary > Tutorials< / summary >
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* [Train Custom Data ](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data ) 🚀 RECOMMENDED
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* [Tips for Best Training Results ](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results ) ☘️
RECOMMENDED
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* [Weights & Biases Logging ](https://github.com/ultralytics/yolov5/issues/1289 ) 🌟 NEW
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* [Supervisely Ecosystem ](https://github.com/ultralytics/yolov5/issues/2518 ) 🌟 NEW
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* [Multi-GPU Training ](https://github.com/ultralytics/yolov5/issues/475 )
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* [PyTorch Hub ](https://github.com/ultralytics/yolov5/issues/36 ) ⭐ NEW
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* [TorchScript, ONNX, CoreML Export ](https://github.com/ultralytics/yolov5/issues/251 ) 🚀
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* [Test-Time Augmentation (TTA) ](https://github.com/ultralytics/yolov5/issues/303 )
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* [Model Ensembling ](https://github.com/ultralytics/yolov5/issues/318 )
* [Model Pruning/Sparsity ](https://github.com/ultralytics/yolov5/issues/304 )
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* [Hyperparameter Evolution ](https://github.com/ultralytics/yolov5/issues/607 )
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* [Transfer Learning with Frozen Layers ](https://github.com/ultralytics/yolov5/issues/1314 ) ⭐ NEW
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* [TensorRT Deployment ](https://github.com/wang-xinyu/tensorrtx )
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< / details >
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## <div align="center">Environments and Integrations</div>
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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.
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< 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 >
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## <div align="center">Compete and Win</div>
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We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with ** $10,000** in cash prizes!
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< 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 >
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## <div align="center">Why YOLOv5</div>
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< 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 >
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< 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 >
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* 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 >
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### Pretrained Checkpoints
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[assets]: https://github.com/ultralytics/yolov5/releases
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|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 | |- |-
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< details >
< summary > Table Notes (click to expand)< / summary >
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* 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`
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< / details >
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## <div align="center">Contribute</div>
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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.
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## <div align="center">Contact</div>
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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 ).
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< br >
< div align = "center" >
< a href = "https://github.com/ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width = "3%" / >
< / a >
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< a href = "https://www.linkedin.com/company/ultralytics" >
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< a href = "https://www.facebook.com/ultralytics" >
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