cleanup and updates

ultralytics · glenn-jocher · Jun 13, 2021 · Jun 8, 2021 · Jun 8, 2021 · Jun 8, 2021
commit f35bda3b6786784f9266f98b377443509251643a
diff --git a/README.md b/README.md
@@ -15,58 +15,61 @@
   <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%"/>
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/>
     </a>
-    <img width="2%" />
+    <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="2%"/>
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/>
     </a>
-    <img width="2%" />
+    <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="2%"/>
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/>
     </a>
-    <img width="2%" />
+    <img width="3%" />
     <a href="https://youtube.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/>
     </a>
-    <img width="2%" />
+    <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="2%"/>
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/>
     </a>
-    <img width="2%" />
+    <img width="3%" />
     <a href="https://www.instagram.com/ultralytics/">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
     </a>
 </div>
 
 <br>
 <p>
-YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset. 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.
+YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. YOLOv5 is current under active development, and all code, models, and documentation are subject to modification or deletion without notice. Use at your own risk.
 </p>
 </div>
 
-### <div align="center">[See YOLOv5 in Action with Our Interactive Demo Here](https://ultralytics.com/yolov5)</div>
+###<div align="center">Try the API</div>
+<div align="center">Instantly run YOLOv5 models using our JSON API.<br> <a href="https://ultralytics.com/yolov5">https://ultralytics.com/yolov5</a> </div>
+
+<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
+<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
+
+
 
-_Note : YOLOv5 is current **under active development**, all code, models, and documentation are subject to modification or deletion without notice. **Use at your own risk.**_
 
 ## <div align="center">Documentation</div>
 
-Check out our [Full Documentation](https://docs.ultralytics.com) or use our Quick Start Tutorials.
+See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
 
 ## <div align="center">Quick Start Tutorials</div>
 
 These tutorials are intended to get you started using YOLOv5 quickly for demonstration purposes.  
-Head to the [Full Documentation](https://docs.ultralytics.com) for more in-depth tutorials.
+Head to the [YOLOv5 Docs](https://docs.ultralytics.com) for more in-depth details.
 
 <details>
 <summary>
 Install Locally
 </summary>
 
 ```bash
-# Clone into current directory
-$ git clone git@github.com:ultralytics/yolov5.git .
-# Install requirements
+$ git clone https://github.com/ultralytics/yolov5
 $ pip install -r requirements.txt
 ```
 
@@ -91,22 +94,22 @@ $ python detect.py --source 0  # webcam
 <details open>
 <summary>Inference Using PyTorch Hub</summary>
 
-This tutorial will automatically download YOLOv5 to your local system before running inference on the supplied image.
+This tutorial will automatically download YOLOv5 models before running inference on the supplied image.
 
 ```python
 import torch
 
-# Define your model, options include yolov5s, yolov5m, yolov5l, yolov5x
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+# Load a model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x
 
-# Define your image
+# Define images
 img = 'https://ultralytics.com/images/zidane.jpg'
 
 # Run inference
 results = model(img)
 
-# Handle your results, options include .print(), .show(), .save(), .panadas().xyz()
-results.print()
+# Handle results
+results.print()  # or .show(), .save(), .pandas().xyz()
 ```
 
 </details>
@@ -155,68 +158,75 @@ Get started with YOLOv5 in less than a few minutes using our integrations.
 
 Add these your toolkit to ensure you get the most out of your training experience:  
 
-* [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) - Debug, compare and reproduce models. Easily visualize performance with powerful custom charts.
-* [Supervisely](https://app.supervise.ly/signup) - Data labeling for images, videos, 3D point cloud, and volumetric medical images  
+* [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) - Build better models faster with experiment tracking, dataset versioning, and model management.
+* [Supervisely](https://app.supervise.ly/signup) - Training and deployment platform for YOLOv5 models. 
 
-## <div align="center">Contribue and Win</div>
+## <div align="center">Contribute and Win</div>
 
-We are super excited to announce our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10000.00** in cash prizes!  
+We are super excited to announce our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!  
 
 <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.png"/>
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"/>
 </a>
 </div>
 
 ## <div align="center">Why YOLOv5</div>
 
-<div align="center">
-
-**Its Fast!**  
-**Its Accurate!**  
-**But above all YOLOv5 is super easy to get up and running due to its PyTorch integration.**
-
-</div>
-
-<br>
-<div align="center">
-<img src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png">
-</div>
+<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>
 
 ### Pretrained Checkpoints
 
 [assets]: https://github.com/ultralytics/yolov5/releases
 
-| 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                    |     | -                  | -                     |
+|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>
 
 <br>
 
 ## <div align="center">Getting Involved and Contributing</div>
 
-Please make sure to read the [Contributing Guide](CONTRIBUTING.md) before making a pull request.
-
-**Thank you to all the people who already contributed to YOLOv5!**
+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. 
 
-Issues should be raised in [GitHub Issues](https://github.com/ultralytics/yolov5/issues) provided yours does not already exist.
 
 ## <div align="center">Get in Touch</div>
 
-**For issues or trouble running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues) and create a new issue provided yours does not already exist.**
-<br>  
-For business or professional support requests please visit:  
-[https://ultralytics.com/contact](https://ultralytics.com/contact)
+- 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>