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.

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

🤖 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

🔥🔥🔥TensorRT-Alpha supports YOLOv8、YOLOv7、YOLOv6、YOLOv5、YOLOv4、v3、YOLOX、YOLOR...🚀🚀🚀CUDA IS ALL YOU NEED.🍎🍎🍎It also supports end2end CUDA C acceleration and multi-batch inference.

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

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

An easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.

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

SimpleAICV:pytorch training and testing examples.

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

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

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

Official Code of Paper "Reversible Column Networks" "RevColv2"

HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

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.

Paddle Large Scale Classification Tools，supports ArcFace, CosFace, PartialFC, Data Parallel + Model Parallel. Model includes ResNet, ViT, Swin, DeiT, CaiT, FaceViT, MoCo, MAE, ConvMAE, CAE.

reproduction of semantic segmentation using masked autoencoder (mae)