how model yaml gets initiated ?

[SONYYA7]

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Question

to train from scratch , we train via yaml file. now there are many layer name like c2, conv,etc those layer are decalred in different place as class , where those layers are loading and how those are intialised? the whole purpose is to initialize the new layer and modify the yolo backbone . i have respective code definition , how to map it and where to example is i want to insert swin transformer in backbone , i have code for that , and how to map it

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[glenn-jocher]

@SONYYA7 hello!

To integrate custom layers like a Swin Transformer into the YOLOv8 backbone, you'll need to modify the model's YAML configuration file and the corresponding Python model definition. Here’s a brief overview of how to proceed:

1. Define Your Layer: Ensure your Swin Transformer layer is defined in a Python file. For instance, if it's defined as a class SwinTransformer, it should be properly imported into the script where you're defining the model architecture.

2. Modify the YAML File: In your model's YAML file, you can replace or insert your new layer by specifying it in the appropriate sequence under the backbone or head sections. For example:

   - from: previous_layer_index
    number: 1
    module: SwinTransformer
    args: [args_if_any]

3. Load and Initialize in Model Definition: In your model definition script (where layers are parsed and assembled into the full model), ensure that your new SwinTransformer is recognized and correctly instantiated. This typically involves modifying the parse_model() function or similar, where each layer specified in the YAML is loaded.

4. Adjustments for Compatibility: Depending on how the new layer interacts with adjacent layers, you might need to adjust input/output dimensions and ensure compatibility.

5. Training: With the YAML file pointing to your modified architecture, you can proceed to train the model. Use the command:

   yolo train data=your_dataset.yaml model=your_modified_model.yaml

This process involves deep integration with the model's architecture, so a thorough understanding of both the existing YOLOv8 architecture and the new components is crucial. If you encounter specific errors or need further customization, feel free to ask more detailed questions in our discussions or issues sections.

Best of luck with your model modifications!