Rust implementations of vision transformer models

Reddit r/ArtificialInteligence Tools
vision-transformer rust deep-learning open-source image-classification transformer

Summary

A Rust crate for building and experimenting with Vision Transformer (ViT) models, providing typed configs, reusable structs, and runnable examples for research and production.

Deep learning in rust, this crate is for building and experimenting with ViT-style image, video, sequence, and self-supervised transformer models in Rust. It provides typed configs, reusable model structs, runnable examples, and shape tests for research prototypes and Rust deep learning projects. Now a Vision Transformer treats an image like a sequence. Normal images have this shape: \[batch, channels, height, width\] The model changes the image into this shape: \[batch, tokens, dim\] The flow is: Split the image into patches. Flatten each patch into one long vector. Project each patch vector into dim. Add position embeddings. Run transformer layers. Pool the tokens. Predict class logits. If you wanna learn more see here: https://github.com/iBz-04/vitch
Original Article

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