Exploring Minor Modifications of the Transformer Architecture for Image Classification

In this blog post, we are going to dive into the modifications made to the transformer architecture for image classification. If you are familiar with transformers and attention mechanisms, this will be a great follow-up article for you.

Transformers lack certain inductive biases found in Convolutional Neural Networks (CNNs), such as translation invariance and locally restricted receptive fields. In simpler terms, transformers struggle with recognizing objects in images when their appearance or position varies.

To address this, the Vision Transformer (ViT) was introduced. Here’s a quick overview of how it works:
1. Split the image into patches
2. Flatten the patches
3. Create lower-dimensional linear embeddings from the patches
4. Add positional embeddings
5. Feed the sequence to a standard transformer encoder
6. Pretrain the model on a large dataset with image labels
7. Finetune the model on a smaller dataset for image classification

The key to ViT’s success lies in converting spatial, non-sequential data (images) into sequences that the transformer can process. By creating sequences of patches from the image, ViT can classify images effectively.

It’s important to note that ViT’s performance improves with a large amount of training data. When trained on datasets with over 14 million images, ViT can compete with or even outperform state-of-the-art CNNs. However, without sufficient data and computational resources, it might be better to stick with traditional CNNs like ResNets or EfficientNets.

The implementation of ViT involves carefully reshaping the image into patches, adding positional embeddings, and utilizing self-attention mechanisms to capture relationships between patches. The architecture of ViT, while similar to the original transformer proposed by Vaswani et al. in 2017, has some unique features tailored for image classification.

Overall, the integration of transformers into image classification tasks represents a significant step forward in the field of computer vision. While there are challenges and limitations, ViT demonstrates the potential for transformers to excel in tasks traditionally dominated by CNNs.

If you’re interested in exploring ViT further, check out the code snippet provided in the blog post and consider diving deeper into the mechanics of ViT in practice. Who knows, you may uncover new insights or ideas that could enhance your understanding of transformers and their applications in image processing.