Vision Transformers (ViT) represent a significant advancement in the field of computer vision, applying concepts from Natural Language Processing (NLP) transformers to the realm of image classification. Traditional convolutional neural networks (CNNs) have dominated image analysis for years; however, the emergence of ViT has demonstrated that transformer architectures can also achieve impressive results when adapted for visual tasks.
The foundational concept of ViT is rooted in the transformer architecture, which revolutionized NLP by effectively capturing long-range dependencies in sequential data through self-attention mechanisms. In a similar manner, ViT processes images by first dividing them into fixed-size patches, treating each patch as a token. These patches are then linearly embedded into vectors, allowing the model to apply the same attention mechanisms used in NLP.
Because the residual skip connection in Vision Transformers (ViT) occurs after the MLP (which essentially consists of a dense or fully-connected layer) in the transformer encoder design, I decided to override the MLP function to integrate this crucial aspect effectively. By adding the skip connection right after the dense layer, I ensure that the input features are combined with the output of the MLP, allowing for a more seamless flow of information. This approach helps mitigate issues related to vanishing gradients and enhances the model's ability to learn complex representations. Incorporating the skip connection in this manner not only improves training stability but also contributes to better performance by facilitating the learning of residual mappings, which can be particularly beneficial in deep networks.
Patches are crucial for tokenizing images in image classification, and they play a significant role in the Vision Transformer architecture. By breaking down images into smaller, manageable segments, the model can better understand and process the visual information. Keras simplifies this seemingly complex task with its tf.image.extract_patches() method, which elegantly handles the intricacies of patch extraction. This method takes essential arguments, such as patch size, stride, padding, and others, allowing for customizable control over how the image is divided. With just a few parameters, it efficiently transforms our images into patches of a defined size, enabling the Vision Transformer to effectively learn from the localized features within each segment. This streamlined approach not only enhances the model's performance but also makes it easier for developers to implement image tokenization in their workflows.
The PatchEncoder layer plays a vital role in transforming image patches. It linearly converts each patch by projecting it into a vector of size Projection_dim , effectively allowing the model to work with fixed-length representations. Additionally, the PatchEncoder incorporates a trainable position embedding, which is added to the projected vector. This position embedding provides crucial spatial information, enabling the model to retain the context of where each patch originated within the original image. By combining both the linear projection and position embedding, the PatchEncoder enhances the model’s ability to understand the relationships between different patches, ultimately improving its performance in image classification tasks. This approach not only facilitates the processing of visual data but also enriches the feature representation learned by the Vision Transformer.