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* added new logo

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* removed old header

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* rocket and W&B fix

* added contributing template

* added social media to top section

* increased size of top social media

* cleanup and updates

* rearrange quickstarts

* API cleanup

* PyTorch Hub cleanup

* Add tutorials

* cleanup

* update CONTRIBUTING.md

* Update README.md

* update wandb link

* Update README.md

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* update environments and integrations

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Co-authored-by: Glenn Jocher <[email protected]>

CONTRIBUTING.md

@@ -0,0 +1,49 @@
+## Contributing to YOLOv5 🚀
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's:
+
+- Reporting a bug
+- Discussing the current state of the code
+- Submitting a fix
+- Proposing a new feature
+- Becoming a maintainer
+
+YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be helping push the frontiers of what's possible in AI 😃!
+
+
+## Submitting a Pull Request (PR) 🛠️
+
+To allow your work to be integrated as seamlessly as possible, we advise you to:
+- ✅ Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master an automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature' with the name of your local branch:
+```bash
+git remote add upstream https://github.com/ultralytics/yolov5.git
+git fetch upstream
+git checkout feature  # <----- replace 'feature' with local branch name
+git merge upstream/master
+git push -u origin -f
+```
+- ✅ Verify all Continuous Integration (CI) **checks are passing**.
+- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  -Bruce Lee
+
+
+## Submitting a Bug Report 🐛
+
+For us to investigate an issue we would need to be able to reproduce it ourselves first. We've created a few short guidelines below to help users provide what we need in order to get started investigating a possible problem.
+
+When asking a question, people will be better able to provide help if you provide **code** that they can easily understand and use to **reproduce** the problem. This is referred to by community members as creating a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces the problem should be:
+
+* ✅ **Minimal** – Use as little code as possible that still produces the same problem
+* ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
+* ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
+
+In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code should be:
+
+* ✅ **Current** – Verify that your code is up-to-date with current GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new copy to ensure your problem has not already been resolved by previous commits.
+* ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
+
+If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better understand and diagnose your problem. 
+
+
+## License
+
+By contributing, you agree that your contributions will be licensed under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/)

README.md

@@ -1,70 +1,136 @@
+<div align="center">
+<p>
 <a align="left" href="https://ultralytics.com/yolov5" target="_blank">
 <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
-&nbsp
-
+</p>
+<br>
+<div>
 <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="Open In Kaggle"></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://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%"/>
+    </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>
+</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>
+
+<!-- 
+<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>
+<summary>
+Install
+</summary>
+
+Python >= 3.6.0 required with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed:
+<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
+```bash
+$ git clone https://github.com/ultralytics/yolov5
+$ pip install -r requirements.txt
+```
+</details>
 
-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.
+<details open>
+<summary>Inference</summary>
 
-<p align="left"><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>
-  
-<p align="left"><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>
-  
-  * 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 test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
-</details>
+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).
 
-- **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.
-- **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.
-- **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP.
-- **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP.
+```python
+import torch
 
+# Model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5x, custom
 
-## Pretrained Checkpoints
+# Images
+img = 'https://ultralytics.com/images/zidane.jpg'  # or file, PIL, OpenCV, numpy, multiple
 
-[assets]: https://github.com/ultralytics/yolov5/releases
+# Inference
+results = model(img)
 
-|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 | |-  |-
+# Results
+results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
+```
 
-<details>
-  <summary>Table Notes (click to expand)</summary>
-  
-  * 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 test.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 test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`  
-  * 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 test.py --data coco.yaml --img 1536 --iou 0.7 --augment`
 </details>
 
 
-## Requirements
 
-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:
-<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
+<details>
+<summary>Inference with detect.py</summary>
+
+`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`.
 ```bash
-$ pip install -r requirements.txt
+$ python detect.py --source 0  # webcam
+                            file.jpg  # image 
+                            file.mp4  # video
+                            path/  # directory
+                            path/*.jpg  # glob
+                            'https://youtu.be/NUsoVlDFqZg'  # YouTube video
+                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
 ```
 
+</details>
+
+<details>
+<summary>Training</summary>
+
+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).
+```bash
+$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
+                                         yolov5m                                40
+                                         yolov5l                                24
+                                         yolov5x                                16
+```
+<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
+
+</details>  
 
-## Tutorials
+<details>
+<summary>Tutorials</summary>
 
 * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)&nbsp; 🚀 RECOMMENDED
 * [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)&nbsp; ☘️ RECOMMENDED
@@ -80,91 +146,126 @@ $ pip install -r requirements.txt
 * [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)&nbsp; ⭐ NEW
 * [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
 
+</details>
 
-## Environments
-
-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):
-
-- **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>
-- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
-- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
-- **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>
-
-
-## Inference
-
-`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`.
-```bash
-$ python detect.py --source 0  # webcam
-                            file.jpg  # image 
-                            file.mp4  # video
-                            path/  # directory
-                            path/*.jpg  # glob
-                            'https://youtu.be/NUsoVlDFqZg'  # YouTube video
-                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
-```
-
-To run inference on example images in `data/images`:
-```bash
-$ python detect.py --source data/images --weights yolov5s.pt --conf 0.25
-
-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'])
-YOLOv5 v4.0-96-g83dc1b4 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)
-
-Fusing layers... 
-Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPs
-image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.010s)
-image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.011s)
-Results saved to runs/detect/exp2
-Done. (0.103s)
-```
-<img width="500" src="https://user-images.githubusercontent.com/26833433/97107365-685a8d80-16c7-11eb-8c2e-83aac701d8b9.jpeg">  
-
-### PyTorch Hub
 
-Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36):
-```python
-import torch
+## <div align="center">Environments and Integrations</div>
 
-# Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+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.
 
-# Image
-img = 'https://ultralytics.com/images/zidane.jpg'
+<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>  
 
-# Inference
-results = model(img)
-results.print()  # or .show(), .save()
-```
 
+## <div align="center">Compete and Win</div>
 
-## Training
+We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!  
 
-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).
-```bash
-$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
-                                         yolov5m                                40
-                                         yolov5l                                24
-                                         yolov5x                                16
-```
-<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
+<div align="center">
+<a href="https://github.com/ultralytics/yolov5/discussions/3213">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"/>
+</a>
+</div>
 
 
-## Citation
+## <div align="center">Why YOLOv5</div>
 
-[![DOI](https://zenodo.org/badge/264818686.svg)](https://zenodo.org/badge/latestdoi/264818686)
+<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>
+  
+<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>
+  
+  * 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 test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+</details>
 
 
-## About Us
+### Pretrained Checkpoints
 
-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:
-- **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.**
-- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**
-- **Custom data training**, hyperparameter evolution, and model exportation to any destination.
+[assets]: https://github.com/ultralytics/yolov5/releases
 
-For business inquiries and professional support requests please visit us at https://ultralytics.com. 
+|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    |   |-     |-
 
+<details>
+  <summary>Table Notes (click to expand)</summary>
+  
+  * 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 test.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 test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`  
+  * 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 test.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+</details>
 
-## Contact
 
-**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 [email protected]. 
+## <div align="center">Contribute</div>
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started. 
+
+
+## <div align="center">Contact</div>
+
+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).
+
+<br>
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