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pix2tex: Using a ViT to convert images of equations into LaTeX code.

An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites

Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.

Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks

Quantized Attention achieves speedup of 2-5x and 3-11x compared to FlashAttention and xformers, without lossing end-to-end metrics across language, image, and video models.

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

Turn any computer or edge device into a command center for your computer vision projects.

A paper list of some recent Transformer-based CV works.

🤖 PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+

ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

SpargeAttention: A training-free sparse attention that can accelerate any model inference.

Extract video features from raw videos using multiple GPUs. We support RAFT flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, and TIMM models.

A PyTorch implementation of "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer"

SimpleAICV:pytorch training and testing examples.

[CVPR 2025 Highlight] Official code and models for Encoder-only Mask Transformer (EoMT).

i. A practical application of Transformer (ViT) on 2-D physiological signal (EEG) classification tasks. Also could be tried with EMG, EOG, ECG, etc. ii. Including the attention of spatial dimension (channel attention) and *temporal dimension*. iii. Common spatial pattern (CSP), an efficient feature enhancement method, realized with Python.

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

FFCS course registration made hassle free for VITians. Search courses and visualize the timetable on the go!

PASSL包含 SimCLR，MoCo v1/v2，BYOL，CLIP，PixPro，simsiam, SwAV, BEiT，MAE 等图像自监督算法以及 Vision Transformer，DEiT，Swin Transformer，CvT，T2T-ViT，MLP-Mixer，XCiT，ConvNeXt，PVTv2 等基础视觉算法