OpenMMLab Detection Toolbox and Benchmark
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Updated
Aug 17, 2024 - Python
OpenMMLab Detection Toolbox and Benchmark
pix2tex: Using a ViT to convert images of equations into LaTeX code.
This repository contains demos I made with the Transformers library by HuggingFace.
Ingest, parse, and optimize any data format ➡️ from documents to multimedia ➡️ for enhanced compatibility with GenAI frameworks
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
SwinIR: Image Restoration Using Swin Transformer (official repository)
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
[GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation!
OpenMMLab Pre-training Toolbox and Benchmark
Scenic: A Jax Library for Computer Vision Research and Beyond
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
EVA Series: Visual Representation Fantasies from BAAI
An all-in-one toolkit for computer vision
[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
EfficientViT is a new family of vision models for efficient high-resolution vision.
This is a collection of our NAS and Vision Transformer work.
VRT: A Video Restoration Transformer (official repository)
[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
The official repo for [NeurIPS'22] "ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation" and [TPAMI'23] "ViTPose : Vision Transformer for Generic Body Pose Estimation"
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