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@@ -1,70 +1,136 @@ |
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<div align="center"> |
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<p> |
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<a align="left" href="https://ultralytics.com/yolov5" target="_blank"> |
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<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a> |
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<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="Open In Kaggle"></a> |
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<br> |
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<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> |
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<a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> |
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<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> |
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<br> |
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<div align="center"> |
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<a href="https://github.com/ultralytics"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/> |
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</a> |
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<img width="2%" /> |
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<a href="https://www.linkedin.com/company/ultralytics"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/> |
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</a> |
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<img width="2%" /> |
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<a href="https://twitter.com/ultralytics"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/> |
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</a> |
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<img width="2%" /> |
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<a href="https://youtube.com/ultralytics"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/> |
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</a> |
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<img width="2%" /> |
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<a href="https://www.facebook.com/ultralytics"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/> |
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</a> |
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<img width="2%" /> |
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<a href="https://www.instagram.com/ultralytics/"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/> |
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</a> |
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</div> |
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<br> |
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<p> |
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YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a> |
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open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. |
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</p> |
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<!-- |
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<a align="center" href="https://ultralytics.com/yolov5" target="_blank"> |
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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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--> |
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</div> |
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## <div align="center">Documentation</div> |
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See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. |
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## <div align="center">Quick Start Examples</div> |
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<details open> |
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<summary> |
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Install |
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</summary> |
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Python >= 3.6.0 required with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed: |
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<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev --> |
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```bash |
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$ git clone https://github.com/ultralytics/yolov5 |
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$ pip install -r requirements.txt |
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``` |
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</details> |
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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. |
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<details open> |
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<summary>Inference</summary> |
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<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p> |
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<details> |
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<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/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p> |
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</details> |
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<details> |
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<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. |
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* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. |
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* **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` |
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</details> |
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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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- **April 11, 2021**: [v5.0 release](https://github.com/ultralytics/yolov5/releases/tag/v5.0): YOLOv5-P6 1280 models, [AWS](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart), [Supervise.ly](https://github.com/ultralytics/yolov5/issues/2518) and [YouTube](https://github.com/ultralytics/yolov5/pull/2752) integrations. |
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- **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. |
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- **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP. |
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- **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP. |
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```python |
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import torch |
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# Model |
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5x, custom |
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## Pretrained Checkpoints |
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# Images |
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img = 'https://ultralytics.com/images/zidane.jpg' # or file, PIL, OpenCV, numpy, multiple |
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[assets]: https://github.com/ultralytics/yolov5/releases |
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# Inference |
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results = model(img) |
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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) |
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|--- |--- |--- |--- |--- |--- |---|--- |--- |
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|[YOLOv5s][assets] |640 |36.7 |36.7 |55.4 |**2.0** | |7.3 |17.0 |
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|[YOLOv5m][assets] |640 |44.5 |44.5 |63.1 |2.7 | |21.4 |51.3 |
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|[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4 |
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|[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8 |
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|[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4 |
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|[YOLOv5m6][assets] |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4 |
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|[YOLOv5l6][assets] |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7 |
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|[YOLOv5x6][assets] |1280 |**54.4** |**54.4** |**72.0** |22.4 | |141.8 |222.9 |
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|[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |- |- |
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# Results |
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results.print() # or .show(), .save(), .crop(), .pandas(), etc. |
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``` |
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<details> |
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<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. |
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* 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` |
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* 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` |
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* All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). |
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* 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 1536 --iou 0.7 --augment` |
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</details> |
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## Requirements |
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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: |
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<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev --> |
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<details> |
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<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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$ pip install -r requirements.txt |
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$ python detect.py --source 0 # webcam |
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file.jpg # image |
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file.mp4 # video |
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path/ # directory |
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path/*.jpg # glob |
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'https://youtu.be/NUsoVlDFqZg' # YouTube video |
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream |
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``` |
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</details> |
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<details> |
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<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 |
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$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 |
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yolov5m 40 |
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yolov5l 24 |
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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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## Tutorials |
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<details> |
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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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@@ -80,91 +146,126 @@ $ pip install -r requirements.txt |
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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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## Environments |
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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): |
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- **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> |
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- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) |
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- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart) |
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- **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> |
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## Inference |
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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 |
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file.jpg # image |
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file.mp4 # video |
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path/ # directory |
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path/*.jpg # glob |
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'https://youtu.be/NUsoVlDFqZg' # YouTube video |
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream |
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``` |
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To run inference on example images in `data/images`: |
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```bash |
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$ python detect.py --source data/images --weights yolov5s.pt --conf 0.25 |
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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']) |
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YOLOv5 v4.0-96-g83dc1b4 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB) |
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Fusing layers... |
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Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPs |
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image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.010s) |
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image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.011s) |
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Results saved to runs/detect/exp2 |
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Done. (0.103s) |
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``` |
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<img width="500" src="https://user-images.githubusercontent.com/26833433/97107365-685a8d80-16c7-11eb-8c2e-83aac701d8b9.jpeg"> |
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### PyTorch Hub |
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Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36): |
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```python |
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import torch |
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## <div align="center">Environments and Integrations</div> |
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# Model |
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') |
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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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# Image |
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img = 'https://ultralytics.com/images/zidane.jpg' |
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<div align="center"> |
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<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/> |
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</a> |
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<a href="https://www.kaggle.com/ultralytics/yolov5"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/> |
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</a> |
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<a href="https://hub.docker.com/r/ultralytics/yolov5"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/> |
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</a> |
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<a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/> |
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</a> |
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<a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/> |
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</a> |
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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-small.png" width="15%"/> |
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</a> |
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</div> |
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# Inference |
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results = model(img) |
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results.print() # or .show(), .save() |
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``` |
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## <div align="center">Compete and Win</div> |
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## Training |
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We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes! |
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Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). |
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```bash |
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$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 |
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yolov5m 40 |
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yolov5l 24 |
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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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<div align="center"> |
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<a href="https://github.com/ultralytics/yolov5/discussions/3213"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"/> |
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</a> |
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</div> |
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## Citation |
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## <div align="center">Why YOLOv5</div> |
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[![DOI](https://zenodo.org/badge/264818686.svg)](https://zenodo.org/badge/latestdoi/264818686) |
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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> |
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<details> |
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<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> |
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</details> |
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<details> |
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<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. |
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* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. |
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* **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` |
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</details> |
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## About Us |
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### Pretrained Checkpoints |
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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: |
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- **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.** |
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- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.** |
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- **Custom data training**, hyperparameter evolution, and model exportation to any destination. |
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[assets]: https://github.com/ultralytics/yolov5/releases |
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For business inquiries and professional support requests please visit us at https://ultralytics.com. |
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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) |
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|--- |--- |--- |--- |--- |--- |---|--- |--- |
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|[YOLOv5s][assets] |640 |36.7 |36.7 |55.4 |**2.0** | |7.3 |17.0 |
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|[YOLOv5m][assets] |640 |44.5 |44.5 |63.1 |2.7 | |21.4 |51.3 |
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|[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4 |
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|[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8 |
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| | | | | | | | | |
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|[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4 |
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|[YOLOv5m6][assets] |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4 |
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|[YOLOv5l6][assets] |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7 |
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|[YOLOv5x6][assets] |1280 |**54.4** |**54.4** |**72.0** |22.4 | |141.8 |222.9 |
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| | | | | | | | | |
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|[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |- |- |
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<details> |
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<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. |
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* 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` |
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* 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` |
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* All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). |
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* 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 1536 --iou 0.7 --augment` |
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</details> |
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## Contact |
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**Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com. |
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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 |
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[https://ultralytics.com/contact](https://ultralytics.com/contact). |
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<br> |
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<div align="center"> |
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<a href="https://github.com/ultralytics"> |
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|
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/> |
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</a> |
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|
<img width="3%" /> |
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<a href="https://www.linkedin.com/company/ultralytics"> |
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|
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/> |
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</a> |
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|
<img width="3%" /> |
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<a href="https://twitter.com/ultralytics"> |
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|
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/> |
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</a> |
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|
<img width="3%" /> |
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<a href="https://youtube.com/ultralytics"> |
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|
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/> |
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</a> |
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<img width="3%" /> |
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<a href="https://www.facebook.com/ultralytics"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/> |
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</a> |
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<img width="3%" /> |
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<a href="https://www.instagram.com/ultralytics/"> |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/> |
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</a> |
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</div> |