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The CNN That Challenges ViT
- Researchers from Meta challenged the idea that ViT's performance superiority solely comes from its transformer-based architecture by applying ViT configuration parameters to ResNet model from 2015, resulting in ConvNeXt which surpassed Swin-T in performance.
- ConvNeXt involves hyperparameter tuning on ResNet model, adjusting macro design, transitioning to ResNeXt architecture, implementing inverted bottleneck structure, exploring kernel sizes, and optimizing micro designs like activation functions and layer normalization.
- Macro design changes include altering stage ratios and the first convolution layer's kernel size and stride to match non-overlapping patch treatment in ViT, improving accuracy slightly.
- ResNeXt-ification involves adjusting group convolution and widening the network to increase accuracy to 80.5% despite a drop due to reduction in model capacity.
- Experimenting with inverted bottleneck structure and kernel sizes, ConvNeXt achieved a peak accuracy of 81.5% by employing separate downsampling layers and reducing batch normalization layers.
- The ConvNeXt architecture is constructed with stem stage, subsequent ConvNeXt blocks in multiple stages, dimension reductions, avgpool layer, and fully-connected output layer, showcasing successful reduction in spatial dimensions and increased channel capacity.
- Implementation of ConvNeXt model involves ConvNeXtBlock and ConvNeXtBlockTransition classes, demonstrating the transition between stages while maintaining accuracy and capacity improvements iteratively.
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