Update export format docstrings (#6151)
* Update export documentation * Cleanup * Update export.py * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update README.md * Update README.md * Update README.md * Update train.py * Update train.py Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@@ -62,15 +62,14 @@ See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on tr
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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
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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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Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
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[**Python>=3.6.0**](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
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$ cd yolov5
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$ pip install -r requirements.txt
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git clone https://github.com/ultralytics/yolov5 # clone
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cd yolov5
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pip install -r requirements.txt # install
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```
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</details>
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@@ -78,8 +77,9 @@ $ pip install -r requirements.txt
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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
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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)
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. [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
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import torch
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@@ -104,17 +104,17 @@ 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
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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](https://github.com/ultralytics/yolov5/tree/master/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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img.jpg # image
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vid.mp4 # video
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path/ # directory
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path/*.jpg # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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python detect.py --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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path/ # directory
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path/*.jpg # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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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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@@ -122,16 +122,20 @@ $ 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
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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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The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
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results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
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and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
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YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
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1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
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largest `--batch-size` possible, or pass `--batch-size -1` for
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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 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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python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
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yolov5s 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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@@ -225,6 +229,7 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
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### Pretrained Checkpoints
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[assets]: https://github.com/ultralytics/yolov5/releases
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[TTA]: https://github.com/ultralytics/yolov5/issues/303
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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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@@ -257,7 +262,6 @@ We love your input! We want to make contributing to YOLOv5 as easy and transpare
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<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a>
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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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