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 是一个专为图像分类设计的 PyTorch Vision Transformer 框架。它实现了一种基于 Transformer 的架构，使用自注意力层和位置感知序列建模而不是卷积滤波器，将图像处理为扁平化补丁序列。 该项目专注于通过知识蒸馏框架进行数据高效训练。该系统允许学生模型模仿高性能教师模型的软标签，以提高准确性和泛化能力，特别是在较小数据集上进行训练时。 该库涵盖了完整的开发生命周期，包括图像分类训练、交叉熵损失优化以及部署预训练权重进行推理。它还包括一个基准测试工具，用于评估模型在标准数据集上的性能和准确性。

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