YOLOv11 + Channel, Temporal, and Spatial Attention (CTSA) mechanism

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I am working on enhancing my YOLOv11 model by incorporating a Channel, Temporal, and Spatial Attention (CTSA) mechanism to improve detection accuracy for complex activities like bag snatching. Could you provide guidance or best practices on how to integrate the CTSA module into the YOLOv11 architecture? Additionally, are there recommended approaches for training the modified model effectively?

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Just remember that anything “custom” is going to be very much something you’ll have to work out for yourself. There’s no real guidance as to how to make such a change since every implementation will be highly subjective.

In general, if you want to create a custom model configuration, you’ll need to generally do the following:

  1. Add new modules into appropriate files under nn/modules/, if you can subclass from any existing modules it’ll probably make your life easier.
  2. Update nn/modules/task.py as needed for including any new modules (usually needed when new modules aren’t subclasses of existing ones)
  3. Create a model YAML file in cfg/models

You might want to review some of the recent-ish publications for new models that were forks of the Ultralytics repo to see how they modified the structure. You can also review this PR which might bypass a few of the above steps.

When it comes to training, you’re going to have to sort that out, as I mentioned previously, once you wonder into custom territory there’s no way to really provide input. Remember that model development is an empirical science, meaning that you just have to run experiments and track the results. It’ll take time, but that’s generally how it works.

One last consideration is that you might want to check out this example as it pertains to action recognition, which sounds like what you’re looking to use. It’s not the exact same as what you’ve stated you’re looking to do, but if the real goal is to have action recognition and not to make modifications to the model, this might be a viable option to get you there.

Good luck and please do share your progress and project!

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