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Update export format docstrings (ultralytics#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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‎README.md‎

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@@ -62,24 +62,24 @@ 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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<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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<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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<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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### 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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<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

‎detect.py‎

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"""
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Run inference on images, videos, directories, streams, etc.
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Usage:
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$ python path/to/detect.py --weights yolov5s.pt --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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Usage - sources:
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$ python path/to/detect.py --weights yolov5s.pt --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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Usage - formats:
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$ python path/to/detect.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s.mlmodel # CoreML (under development)
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yolov5s_openvino_model # OpenVINO (under development)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow protobuf
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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yolov5s.engine # TensorRT
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"""
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import argparse

‎export.py‎

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"""
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Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
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Format | Example | `--include ...` argument
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--- | --- | ---
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PyTorch | yolov5s.pt | -
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TorchScript | yolov5s.torchscript | `torchscript`
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ONNX | yolov5s.onnx | `onnx`
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CoreML | yolov5s.mlmodel | `coreml`
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OpenVINO | yolov5s_openvino_model/ | `openvino`
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TensorFlow SavedModel | yolov5s_saved_model/ | `saved_model`
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TensorFlow GraphDef | yolov5s.pb | `pb`
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TensorFlow Lite | yolov5s.tflite | `tflite`
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TensorFlow.js | yolov5s_web_model/ | `tfjs`
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TensorRT | yolov5s.engine | `engine`
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Format | Example | `--include ...` argument
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--- | --- | ---
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PyTorch | yolov5s.pt | -
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TorchScript | yolov5s.torchscript | `torchscript`
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ONNX | yolov5s.onnx | `onnx`
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CoreML | yolov5s.mlmodel | `coreml`
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OpenVINO | yolov5s_openvino_model/ | `openvino`
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TensorFlow SavedModel | yolov5s_saved_model/ | `saved_model`
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TensorFlow GraphDef | yolov5s.pb | `pb`
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TensorFlow Lite | yolov5s.tflite | `tflite`
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TensorFlow Edge TPU | yolov5s_edgetpu.tflite | `edgetpu`
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TensorFlow.js | yolov5s_web_model/ | `tfjs`
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TensorRT | yolov5s.engine | `engine`
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$ python path/to/export.py --weights yolov5s.pt --include torchscript onnx coreml openvino saved_model tflite tfjs
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yolov5s_saved_model
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yolov5s.pb
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yolov5s.tflite
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yolov5s_edgetpu.tflite
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yolov5s.engine
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TensorFlow.js:

‎train.py‎

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Train a YOLOv5 model on a custom dataset
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Train a YOLOv5 model on a custom dataset.
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Models and datasets download automatically from the latest YOLOv5 release.
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Models: https://github.com/ultralytics/yolov5/tree/master/models
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Datasets: https://github.com/ultralytics/yolov5/tree/master/data
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Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
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$ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (RECOMMENDED)
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$ python path/to/train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
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"""
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import math
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‎val.py‎

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Validate a trained YOLOv5 model accuracy on a custom dataset
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$ python path/to/val.py --data coco128.yaml --weights yolov5s.pt --img 640
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$ python path/to/val.py --weights yolov5s.pt --data coco128.yaml --img 640
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Usage - formats:
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$ python path/to/val.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s.mlmodel # CoreML (under development)
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yolov5s_openvino_model # OpenVINO (under development)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow protobuf
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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yolov5s.engine # TensorRT
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"""
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import argparse

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