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

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

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

Open-source evaluation toolkit of large vision-language models (LVLMs), support 160+ VLMs, 50+ benchmarks

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

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.

SimpleAICV:pytorch training and testing examples.

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

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

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

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.

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

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.

A ViT based transformer applied on multi-channel time-series EEG data for motor imagery classification

Vision Transformer using TensorFlow 2.0

Implementation of transformers based architecture in PyTorch.

Mimix: A Text Generation Tool and Pretrained Chinese Models

Simple and easy to understand PyTorch implementation of Vision Transformer (ViT) from scratch with detailed steps. Tested on small datasets: MNIST, FashionMNIST, SVHN, CIFAR10, and CIFAR100.

reproduction of semantic segmentation using masked autoencoder (mae)

Training ImageNet / CIFAR models with sota strategies and fancy techniques such as ViT, KD, Rep, etc.