pix2tex: Using a ViT to convert images of equations into LaTeX code.
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Updated
May 6, 2024 - Python
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
🤖 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.
[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
A paper list of some recent Transformer-based CV works.
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, ResNet features.
A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.
FFCS course registration made hassle free for VITians. Search courses and visualize the timetable on the go!
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
Open-source evaluation toolkit of large vision-language models (LVLMs), support GPT-4v, Gemini, QwenVLPlus, 40+ HF models, 20+ benchmarks
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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.
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.
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