How To modified YOLO

To facilitate easy customization of the YOLO model, we've structured the codebase to allow for changes through configuration files and minimal code adjustments. This guide will walk you through the steps to customize various components of the model including the architecture, blocks, data loaders, and loss functions.

Custom Model Architecture

You can change the model architecture simply by modifying the YAML configuration file. Here's how:

Modify Architecture in Config:

Navigate to your model's configuration file (typically formate like yolo/config/model/v9-c.yaml).
- Adjust the architecture settings under the architecture section. Ensure that every module you reference exists in module.py, or refer to the next section on how to add new modules.
```
model:
  foo:
    - ADown:
        args: {out_channels: 256}
    - RepNCSPELAN:
        source: -2
        args: {out_channels: 512, part_channels: 256}
        tags: B4
  bar:
    - Concat:
        source: [-2, B4]
```
tags: Use this to labels any module you want, and could be the module source.

source: Set this to the index of the module output you wish to use as input; default is -1 which refers to the last module's output. Capable tags, relative position, absolute position

args: A dictionary used to initialize parameters for convolutional or bottleneck layers.

output: Whether to serve as the output of the model.

Custom Block

To add or modify a block in the model:

Create a New Module:

Define a new class in module.py that inherits from nn.Module.

The constructor should accept in_channels as a parameter. Make sure to calculate out_channels based on your model's requirements or configure it through the YAML file using args.
```
class CustomBlock(nn.Module):
    def __init__(self, in_channels, out_channels, **kwargs):
        super().__init__()
        self.module = # conv, bool, ...
    def forward(self, x):
        return self.module(x) 
```

Reference in Config:

 ...
 - CustomBlock:
     args: {out_channels: int, etc: ...}
     ...
 ...

Custom Data Augmentation

Custom transformations should be designed to accept an image and its bounding boxes, and return them after applying the desired changes. Here’s how you can define such a transformation:

Define Dataset:

Your class must have a __call__ method that takes a PIL image and its corresponding bounding boxes as input, and returns them after processing.

 class CustomTransform:
     def __init__(self, prob=0.5):
         self.prob = prob

     def __call__(self, image, boxes):
         return image, boxes

Update CustomTransform in Config:

Specify your custom transformation in a YAML config yolo/config/data/augment.yaml. For examples:
```
Mosaic: 1
# ... (Other Transform)
CustomTransform: 0.5
```