When it comes to image classification, every percentage point of accuracy counts. In my recent experiments with attention modules on CLIP RN50, I stumbled upon some surprising results that left me scratching my head.
To start, I fine-tuned the classification layer of CLIP RN50 and reached a decent 84% accuracy on my dataset. But I wanted more. So, I decided to experiment with adding attention modules to see if I could squeeze out more performance.
The Unexpected Twist
I added CBAM (Convolutional Block Attention Module), which combines both channel and spatial attention. To my surprise, accuracy improved to 89% – a solid boost. But then I tried SENet (Squeeze-and-Excitation Network), which only does channel attention. And that’s when things got interesting: I got an even better result – 93% accuracy.
But here’s the thing: I expected CBAM to outperform SENet. After all, CBAM combines two types of attention, while SENet only focuses on channel attention. So, what’s going on?
Possible Explanations
There are a few possible reasons why SENet outperformed CBAM in my experiment. Maybe it’s due to the characteristics of my dataset or the way I integrated CBAM into CLIP. Or perhaps it’s related to my training setup. I’d love to hear from others who have tried attention modules on CLIP or ResNet backbones – have you seen similar results?
The Takeaway
Attention modules can be a powerful tool in image classification, but it’s clear that there’s more to it than just slapping on an attention mechanism. Understanding how these modules interact with your model and dataset is key to getting the most out of them.
If you’ve had similar experiences or insights, I’d love to hear about them in the comments!