This project is a research-focused toolkit for deep learning image classification and multimodal analysis. It provides a library of transformer-based architectures and multi-layer perceptron models designed to process visual data by treating images as sequences of patches rather than relying on traditional convolutional operations.
The framework distinguishes itself by enabling cross-modal analysis through a shared latent vector space, which allows for image-text retrieval and zero-shot classification. By mapping visual and textual inputs into a unified numerical representation, the library facilitates direct similarity comparisons between different media types without requiring task-specific training data.
The toolkit supports a range of research workflows, including the fine-tuning of pre-trained model checkpoints on custom datasets and the evaluation of model performance. Users can access a collection of serialized model weights to initialize projects, while command-line utilities provide automation for adapting these models to specific visual recognition tasks.
