pix2tex: Using a ViT to convert images of equations into LaTeX code.
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Updated
Dec 5, 2024 - Python
pix2tex: Using a ViT to convert images of equations into LaTeX code.
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
Turn any computer or edge device into a command center for your computer vision projects.
🤖 PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep 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.
A PyTorch implementation of "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer"
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
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.
Official Code of Paper "Reversible Column Networks" "RevColv2"
My implementation of "Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution"
MoH: Multi-Head Attention as Mixture-of-Head Attention
A ViT based transformer applied on multi-channel time-series EEG data for motor imagery classification
reproduction of semantic segmentation using masked autoencoder (mae)
Open source implementation of "Vision Transformers Need Registers"
Mimix: A Text Generation Tool and Pretrained Chinese 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.
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