Questions about C3 and C3x modules

[Zengyf-CVer]

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Question

@glenn-jocher I am researching two modules, C3 and C3x, and I found that there is a difference between line 200 and line 214, but the parameter k has changed, can you explain the difference between these two modules in detail?At the same time, I don't quite understand the meaning of "cross-convolutions" in the comment, where is it applied in the code? https://github.com/ultralytics/ultralytics/blob/4e08e122564a7a09693bb8df25a93c5f09db5010/ultralytics/nn/modules/block.py#L191-L214

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[glenn-jocher]

@Zengyf-CVer k=((1, 1), (3, 3)) just means that the repeated convolutions will be 1x1 followed by 3x3, repeating. This was used in YOLOv5.

In YOLOv8 we used k=((3, 3), (3, 3)), so we are simply repeating the same 3x3 convolution many times.

"Cross convolution is just a term I came up with for a vertical kernel followed by a horizontal kernel, i.e. k=((1, 3), (3, 1))

[Zengyf-CVer]

@glenn-jocher What does this cross-convolution do? Or what is the original intention of your design? I can understand that it has a certain effect on multi-size targets?

[glenn-jocher]

@Zengyf-CVer The cross-convolution that we used in YOLOv8 is a 3x3 convolution that first convolves across the height of the feature map and then across its width. We use this convolution in the C3 and C3x modules in our architecture. The purpose is to impose horizontal and vertical feature interaction, allowing the network to more effectively learn useful features which help improve detection performance, especially for multi-scale objects. These modules allow for better detection and have been shown to improve mean Average Precision (mAP) performance on certain datasets.

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[AhmedSharba]

I need the block diagram of C3x and where can add it to yolov5 to improve the accuracy

[glenn-jocher]

@AhmedSharba hi there! 👋 The C3x block is a component of the YOLOv8 architecture designed to enhance feature extraction. To visualize its structure, you might want to check out the YOLOv8 model diagrams in our documentation or the model summary generated by our code.

Incorporating C3x into YOLOv5 would require modifying the YOLOv5's architecture to replace the existing modules with C3x blocks. This can be a complex task as it involves understanding both architectures deeply and ensuring the dimensions align correctly after the modifications.

Here's a very basic example of how you might start this process in code:

from models.common import C3x  # Import C3x module

# Replace C3 modules with C3x in the YOLOv5 model definition
# This is a simplified example and actual integration may be more complex
class Model(nn.Module):
    def __init__(self, ...):
        super(Model, self).__init__()
        ...
        self.c3x = C3x(...)
        ...

    def forward(self, x):
        ...
        x = self.c3x(x)  # Use C3x block
        ...

Remember, this is a non-trivial change and requires careful tuning and validation to ensure improved accuracy. Good luck! 🚀

[AhmedSharba]

@glenn-jocher thank you for reply, I have another two questions: 1- what happened if I replace C3 with C3x now I try it and not error occurred 2- what is the module from existing modules can replace one or more of C3 to improve accuracy on DOTA dataset for only two classes car and plane

[glenn-jocher]

Hi @AhmedSharba! Great to hear you're experimenting with the modules! 🛠️

1. Replacing C3 with C3x and not encountering errors is a good sign. It means the dimensions are compatible. Keep an eye on the training metrics to see if the change benefits your model's performance.

2. For the DOTA dataset, which has aerial views, you might want to try modules that capture spatial relationships effectively. The SPP (Spatial Pyramid Pooling) or PAN (Path Aggregation Network) modules could be beneficial as they help the model focus on multi-scale features, which is useful for detecting objects like cars and planes from various altitudes and angles.

Remember, each change should be followed by careful training and evaluation to measure its impact. Happy coding! ✈️🚗