Awesome GitHub RepositoriesPatch Embedding Modules

This library provides a comprehensive collection of modular building blocks and research-backed architectures for implementing vision transformers within the PyTorch framework. It serves as a centralized repository for constructing, training, and analyzing attention-based models, offering a wide array of specialized variants designed for image classification and visual representation learning. The project distinguishes itself through a focus on architectural efficiency and flexibility, supporting diverse input formats including non-square images and volumetric data like video. It incorporates

This project is a research library and toolkit for deep learning computer vision, focused on implementing transformer and mixer-based architectures for image classification. It processes visual data by converting images into sequences of patches, allowing standard attention mechanisms to capture global dependencies without relying on traditional convolutional operations. The framework distinguishes itself through its support for multimodal embedding analysis, which maps images and text into a shared latent vector space. This capability enables zero-shot classification and cross-modal retrieva

Code release for ConvNeXt model

PlugNPlay-Modules is a collection of reusable PyTorch computer vision modules and deep learning architectural components. It provides a library of standardized building blocks for constructing neural networks, focusing on attention mechanisms, signal processing layers, and feature fusion modules. The project is distinguished by its extensive variety of attention primitives, covering spatial, channel, and temporal weighting, as well as specialized variants like deformable, frequency-enhanced, and linear-complexity attention. It also implements advanced signal processing tools within the neural

DeiT est un framework de vision transformer PyTorch conçu pour la classification d'images. Il implémente une architecture basée sur des transformers qui traite les images comme des séquences de patchs aplatis en utilisant des couches d'auto-attention et une modélisation de séquence sensible à la position au lieu de filtres convolutifs. Le projet se concentre sur l'entraînement efficace en données grâce à un framework de distillation de connaissances. Ce système permet à un modèle étudiant d'imiter les soft labels d'un modèle enseignant haute performance pour améliorer la précision et la généralisation, particulièrement lors de l'entraînement sur des jeux de données plus petits. La bibliothèque couvre le cycle de vie complet du développement, incluant l'entraînement à la classification d'images, l'optimisation de la perte d'entropie croisée et le déploiement de poids pré-entraînés pour l'inférence. Elle inclut également un outil de benchmarking pour évaluer les performances et la précision du modèle par rapport aux jeux de données standard.

SegFormer is a semantic segmentation framework and transformer-based model designed for pixel-level image classification. It provides a deep learning architecture that assigns class labels to pixels using a hierarchical transformer encoder and a multi-layer perceptron decoder. The framework utilizes a hierarchical transformer encoder to process multi-scale features through a pyramid of blocks and an all-MLP decoder to aggregate these features without complex attention mechanisms. It incorporates overlap patch embedding to preserve local continuity and sequential self-attention reduction to ma