30 open-source projects similar to wofmanaf/rest, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.
This repository contains PyTorch code for ConViT. It builds on code from the Data-Efficient Vision Transformer and from timm.
DeiT is a PyTorch vision transformer framework designed for image classification. It implements a transformer-based architecture that processes images as sequences of flattened patches using self-attention layers and position-aware sequence modeling instead of convolutional filters. The project focuses on data-efficient training through a knowledge distillation framework. This system allows a student model to mimic the soft labels of a high-performance teacher model to improve accuracy and generalization, particularly when training on smaller datasets. The library covers the full development
Neighborhood Attention Transformer, arxiv 2022 / CVPR 2023. Dilated Neighborhood Attention Transformer, arxiv 2022
This repo is an official implementation for "Dynamic Grained Encoder for Vision Transformers" (NeurIPS2021) on PyTorch framework.
This project provides the source code for the vision longformer paper.
This repository contains the PyTorch training and evaluation code for LocalViT.
This repository contains PyTorch implementation for DynamicViT (NeurIPS 2021).
Preprint Link: Escaping the Big Data Paradigm with Compact Transformers
This is a collection of our NAS and Vision Transformer work.
This repository contains the official implementation of the research paper, "FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization" ICCV 2023
:robot: PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
Efficient-AI-Backbones is a lightweight neural network library and computer vision model zoo. It provides a collection of optimized deep learning backbones designed to minimize computational overhead and memory usage for artificial intelligence tasks. The project implements specialized architectures such as GhostNet and MLP to reduce processing requirements. It features a modular backbone design and the distribution of pretrained weights to accelerate the development and deployment of vision models. The library covers efficient neural network design and edge device AI optimization. Its capab