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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 >
< a href = "https://zenodo.org/badge/latestdoi/264818686" > < img src = "https://zenodo.org/badge/264818686.svg" alt = "YOLOv5 Citation" > < / 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 >
< 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://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg" > < img src = "https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt = "Join Forum" > < / a >
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< / div >
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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 = "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%" / >
< / a >
< img width = "2%" / >
< a href = "https://twitter.com/ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width = "2%" / >
< / a >
< img width = "2%" / >
< a href = "https://youtube.com/ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width = "2%" / >
< / a >
< img width = "2%" / >
< a href = "https://www.facebook.com/ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width = "2%" / >
< / a >
< img width = "2%" / >
< 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 >
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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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<!--
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< a align = "center" href = "https://ultralytics.com/yolov5" target = "_blank" >
< img width = "800" src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png" > < / a >
-->
< / 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 >
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< summary > Install< / summary >
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Clone repo and install [requirements.txt ](https://github.com/ultralytics/yolov5/blob/master/requirements.txt ) in a
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[**Python>=3.7.0** ](https://www.python.org/ ) environment, including
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[**PyTorch>=1.7** ](https://pytorch.org/get-started/locally/ ).
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```bash
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git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
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```
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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 ](https://github.com/ultralytics/yolov5/tree/master/models ) download automatically 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 ](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` .
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```bash
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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
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```
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< / details >
< details >
< summary > Training< / summary >
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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.
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```bash
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python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16
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```
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< img width = "800" src = "https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" >
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< / 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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* [Roboflow for Datasets, Labeling, and Active Learning ](https://github.com/ultralytics/yolov5/issues/4975 ) 🌟 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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* [TFLite, ONNX, CoreML, TensorRT 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</div>
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Get started in seconds with our verified environments. 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 >
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< / div >
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## <div align="center">Integrations</div>
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< div align = "center" >
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< a href = "https://wandb.ai/site?utm_campaign=repo_yolo_readme" >
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< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width = "49%" / >
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< / a >
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< 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 >
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|Weights and Biases|Roboflow ⭐ NEW|
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|:-:|:-:|
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|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 ) |
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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 >
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< / p > -->
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## <div align="center">Why YOLOv5</div>
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< p align = "left" > < img width = "800" src = "https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png" > < / p >
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< details >
< summary > YOLOv5-P5 640 Figure (click to expand)< / summary >
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< p align = "left" > < img width = "800" src = "https://user-images.githubusercontent.com/26833433/136763877-b174052b-c12f-48d2-8bc4-545e3853398e.png" > < / p >
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< / details >
< details >
< summary > Figure Notes (click to expand)< / summary >
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* **COCO AP val** denotes mAP@0.5:0.95 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`
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< / details >
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### Pretrained Checkpoints
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[assets]: https://github.com/ultralytics/yolov5/releases
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[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
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|[YOLOv5s6][assets] |1280 |44.5 |63.0 |385 |8.2 |3.6 |12.6 |16.8
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|[YOLOv5m6][assets] |1280 |51.0 |69.0 |887 |11.1 |6.8 |35.7 |50.0
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|[YOLOv5l6][assets] |1280 |53.6 |71.6 |1784 |15.8 |10.5 |76.7 |111.4
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|[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 > -
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< details >
< summary > Table Notes (click to expand)< / summary >
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* All checkpoints are trained to 300 epochs with default settings and hyperparameters.
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* **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`
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* **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`
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* **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`
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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, 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 >
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## <div align="center">Contact</div>
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For YOLOv5 bugs and feature requests please visit [GitHub Issues ](https://github.com/ultralytics/yolov5/issues ). For business inquiries or
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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 >
< img width = "3%" / >
< a href = "https://www.linkedin.com/company/ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width = "3%" / >
< / a >
< img width = "3%" / >
< a href = "https://twitter.com/ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width = "3%" / >
< / a >
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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 = "3%" / >
< / a >
< img width = "3%" / >
< a href = "https://www.facebook.com/ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width = "3%" / >
< / a >
< img width = "3%" / >
< a href = "https://www.instagram.com/ultralytics/" >
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