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docs: Update train_model.md to include instructions for training and using custom detection models
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README.md

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- Make available detection information for other plugins through settings
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- More real-time models choices
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## Train a custom model
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## Train and use a custom detection model
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Follow the instructions in [docs/train_model.md](docs/train_model.md) to train your own custom model.
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Follow the instructions in [docs/train_model.md](docs/train_model.md) to train and use your own custom model.
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## Building
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docs/image.png

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docs/train_model.md

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# How to train your custom model for OBS Detect plugin
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# How to train and use a custom model with OBS Detect plugin
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OBS Detect is based on the [EdgeYOLO](https://github.com/LSH9832/edgeyolo) work.
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They provide a model training script that works with just setting some parameters.
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If you already have a trained EdgeYOLO model in `.onnx` format, skip to the [last step](#step-6-use-the-model-with-obs-detect).
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You need to get a dataset first. The supported dataset formats are mentiond in the [EdgeYOLO](https://github.com/LSH9832/edgeyolo?tab=readme-ov-file#train) readme: COCO, VOC, YOLO, and DOTA.
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In this example we will use a COCO dataset from Roboflow. You can get the dataset from [here](https://public.roboflow.com/object-detection/aquarium/2).
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You can now use the ONNX model with the OBS Detect plugin. Just load the model from the plugin settings.
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![alt text](image.png)
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![select external model](image.png)
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You will also need a configuration file for the model but that is created automatically by the export script above. It will have the same name as the ONNX model but with a `.json` extension.
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You will also need a configuration file for the model with the class names, which is created automatically by the export / conversion script above. It will have the same name as the ONNX model but with a `.json` extension.

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