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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 >
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English | [简体中文 ](.github/README_cn.md )
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< br >
< div >
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< a href = "https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml" > < img src = "https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/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 >
< 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 >
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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 align = "center" >
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< a href = "https://github.com/ultralytics" style = "text-decoration:none;" >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width = "2%" alt = "" / > < / a >
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< a href = "https://www.linkedin.com/company/ultralytics" style = "text-decoration:none;" >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width = "2%" alt = "" / > < / a >
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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 >
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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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YOLOv5 [PyTorch Hub ](https://github.com/ultralytics/yolov5/issues/36 ) inference. [Models ](https://github.com/ultralytics/yolov5/tree/master/models ) download automatically from the latest
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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 yolov5n - yolov5x6, 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
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'path/*.jpg' # glob
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'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 )
- [Tips for Best Training Results ](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results )
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RECOMMENDED
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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 ) 🚀
- [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 )
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- [Transfer Learning with Frozen Layers ](https://github.com/ultralytics/yolov5/issues/1314 )
- [Architecture Summary ](https://github.com/ultralytics/yolov5/issues/6998 ) 🌟 NEW
- [Weights & Biases Logging ](https://github.com/ultralytics/yolov5/issues/1289 )
- [Roboflow for Datasets, Labeling, and Active Learning ](https://github.com/ultralytics/yolov5/issues/4975 )
- [ClearML Logging ](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml ) 🌟 NEW
- [Deci Platform ](https://github.com/ultralytics/yolov5/wiki/Deci-Platform ) 🌟 NEW
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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" >
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< 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 = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "5%" alt = "" / >
< a href = "https://www.kaggle.com/ultralytics/yolov5" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "5%" alt = "" / >
< 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 = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "5%" alt = "" / >
< 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 = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "5%" alt = "" / >
< 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 = "10%" / > < / 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://bit.ly/yolov5-deci-platform" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "14%" height = "0" alt = "" / >
< a href = "https://cutt.ly/yolov5-readme-clearml" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "14%" height = "0" alt = "" / >
< a href = "https://roboflow.com/?ref=ultralytics" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width = "14%" height = "0" alt = "" / >
< a href = "https://wandb.ai/site?utm_campaign=repo_yolo_readme" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb.png" width = "10%" / > < / a >
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< / div >
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|Deci ⭐ NEW|ClearML ⭐ NEW|Roboflow|Weights & Biases
|:-:|:-:|:-:|:-:|
|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci ](https://bit.ly/yolov5-deci-platform )|Automatically track, visualize and even remotely train YOLOv5 using [ClearML ](https://cutt.ly/yolov5-readme-clearml ) (open-source!)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow ](https://roboflow.com/?ref=ultralytics ) |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases ](https://wandb.ai/site?utm_campaign=repo_yolo_readme )
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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/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.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/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.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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| 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) |
|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|
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| [YOLOv5n ](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt ) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
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| [YOLOv5s ](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt ) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
| [YOLOv5m ](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt ) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
| [YOLOv5l ](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt ) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
| [YOLOv5x ](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt ) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
| | | | | | | | | |
| [YOLOv5n6 ](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt ) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
| [YOLOv5s6 ](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt ) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
| [YOLOv5m6 ](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt ) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
| [YOLOv5l6 ](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt ) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
| [YOLOv5x6 ](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt )< br > + [TTA][TTA] | 1280< br > 1536 | 55.0< br > **55.8** | 72.7< br > **72.7** | 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. Nano and Small models use [hyp.scratch-low.yaml ](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml ) hyps, all others use [hyp.scratch-high.yaml ](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml ).
- **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`
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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!
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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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[assets]: https://github.com/ultralytics/yolov5/releases
[tta]: https://github.com/ultralytics/yolov5/issues/303
