vit-pytorch - Implementing Vision Transformers in Pytorch for Advanced Image Classification

Vision Transformer - Pytorch

The Vision Transformer (ViT) project offers a Pytorch implementation of the Vision Transformer model, an innovative approach to achieving state-of-the-art performance in image classification using just a single transformer encoder. This project aims to streamline the process of implementing attention-based models in computer vision, thus contributing to what is often referred to as the 'attention revolution' in artificial intelligence.

To get started with the ViT implementation, you can install it using pip:

$ pip install vit-pytorch

Usage

The ViT can be readily integrated into your projects with the following Python script:

import torch
from vit_pytorch import ViT

v = ViT(
    image_size=256,  # size of the input image
    patch_size=32,   # size of each patch in the image
    num_classes=1000, # total number of classes for classification
    dim=1024,        # dimension of the feature space
    depth=6,         # number of transformer blocks
    heads=16,        # number of attention heads
    mlp_dim=2048,    # dimension of the feedforward network
    dropout=0.1,     # dropout rate for regularization
    emb_dropout=0.1  # dropout rate for the embedding layer
)

img = torch.randn(1, 3, 256, 256)  # example image tensor with batch size, channels, height, and width

preds = v(img)  # forward pass through the model

Parameters

Core Parameters

image_size: Specifies the size of the input image. For non-square images, the larger dimension should fit this size.
patch_size: Defines the size of each image patch and should evenly divide the image_size.
num_classes: Number of target classes for prediction.
dim: Final dimension of the output tensor after the linear transformation.
depth: Number of transformer encoder blocks.
heads: Number of attention heads in each multi-headed attention mechanism.
mlp_dim: Dimensionality of the feed-forward network.
channels: Default is set to 3 for RGB images.
dropout: Dropout rate, a technique to prevent overfitting.
emb_dropout: Dropout rate applied to the embedding layer.
pool: Selection between cls token pooling and mean pooling methods.

Extended Models

Apart from the standard Vision Transformer, this project includes several variants tailored for different tasks:

Simple ViT: Simplified version with faster training cycles and reduced use of dropout and augmentation techniques.
NaViT: Utilizes masking and attention variability for processing multi-resolution images.
Deep ViT: Enhances depth scalability with techniques like Re-attention to improve attention in deeper networks.
CaiT: Incorporates targeted modifications for deeper architectures, facilitating efficient sequences-to-sequences learning.
Token-to-Token ViT: Introduces overlap between tokens at initial layers for enriched feature extraction.

Each extended model in the ViT-Pytorch project is creatively adapted to particular research elements and practical considerations, allowing for experimentation and broader adoption in diverse AI and machine learning contexts. The repository not only provides comprehensive model implementations but also instructive examples and resources for ongoing learning and exploration.

This project is a powerful testament to the adaptability and potential of transformer models in computer vision, paving the way for more intuition-guided and empirically backed model experimentation and adoption in AI-driven applications.