PyCharm reformat (#4209)
* PyCharm reformat * YAML reformat * Markdown reformat
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@@ -52,31 +52,33 @@ YOLOv5 🚀 is a family of object detection architectures and models pretrained
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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>Install</summary>
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[**Python>=3.6.0**](https://www.python.org/) is required with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
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[**Python>=3.6.0**](https://www.python.org/) is required with all
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[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including
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[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
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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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$ cd yolov5
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$ pip install -r requirements.txt
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```
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</details>
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<details open>
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<summary>Inference</summary>
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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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Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
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from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
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```python
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import torch
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@@ -85,7 +87,7 @@ import torch
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom
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# Images
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img = 'https://ultralytics.com/images/zidane.jpg' # or PosixPath, PIL, OpenCV, numpy, list
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img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
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# Inference
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results = model(img)
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@@ -101,7 +103,9 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
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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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`detect.py` runs inference on a variety of sources, downloading models automatically from
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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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@@ -117,13 +121,18 @@ $ python detect.py --source 0 # webcam
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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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Run commands below to reproduce results
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on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on
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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
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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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@@ -132,7 +141,8 @@ $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size
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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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* [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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* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW
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* [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518) 🌟 NEW
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* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
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@@ -147,10 +157,11 @@ $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size
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</details>
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## <div align="center">Environments and Integrations</div>
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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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Get started in seconds with our verified environments and integrations,
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including [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) for automatic YOLOv5 experiment
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logging. 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">
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@@ -173,33 +184,33 @@ Get started in seconds with our verified environments and integrations, includin
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</a>
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</div>
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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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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">
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<a href="https://github.com/ultralytics/yolov5/discussions/3213">
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<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="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 val.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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* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size
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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
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`python val.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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### Pretrained Checkpoints
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@@ -221,24 +232,30 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
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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 val.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 val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half`
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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 val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
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</details>
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* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results
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denote val2017 accuracy.
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* AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP**
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by `python val.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
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GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and
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includes FP16 inference, postprocessing and NMS. **Reproduce speed**
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by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half`
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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
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augmentation. **Reproduce TTA** 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.
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We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see
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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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For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or
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professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
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<br>
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