Adding CBAM operator
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- Dominant language
- Python
- Stars
- 17.9k
- Forks
- 7.3k
- Avg merge
- 1d 15h
- Merged PRs (30d)
- 13
Description
🚀 The feature
Implement CBAM (Convolutional Block Attention Module). It is an attention operator released in 2018 (paper), similar to Squeeze and Excitation but with the addition of spatial attention.
Motivation, pitch
In some cases, CBAM presents better results than SE due to the addition of spatial attention. SE uses only global channel-wise attention (each channel is scaled by the same value), while CBAM computes an attention map for each channel, scaling "by regions", learning more granular attention.
Alternatives
No response
Additional context
No response
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked CBAM paper and compare its channel and spatial attention design with the repository's existing Squeeze-and-Excitation support. Identify the appropriate model entry point and establish that the implementation matches the paper's behavior before considering the work done.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100