keras-vision-transformerThe Tensorflow, Keras implementation of Swin-Transformer and Swin-UNET
Stars: ✭ 91 (-54.73%)
Mutual labels: transformer, vision-transformer
image-classificationA collection of SOTA Image Classification Models in PyTorch
Stars: ✭ 70 (-65.17%)
Mutual labels: transformer, vision-transformer
YOLOSYou Only Look at One Sequence (NeurIPS 2021)
Stars: ✭ 612 (+204.48%)
Mutual labels: transformer, vision-transformer
SwinIRSwinIR: Image Restoration Using Swin Transformer (official repository)
Stars: ✭ 1,260 (+526.87%)
Mutual labels: transformer, vision-transformer
semantic-segmentationSOTA Semantic Segmentation Models in PyTorch
Stars: ✭ 464 (+130.85%)
Mutual labels: transformer, vision-transformer
towheeTowhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
Stars: ✭ 821 (+308.46%)
Mutual labels: transformer, vision-transformer
SReTOfficial PyTorch implementation of our ECCV 2022 paper "Sliced Recursive Transformer"
Stars: ✭ 51 (-74.63%)
Mutual labels: vision-transformer, efficient-transformers
Restormer[CVPR 2022--Oral] Restormer: Efficient Transformer for High-Resolution Image Restoration. SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.
Stars: ✭ 586 (+191.54%)
Mutual labels: transformer, efficient-transformers
LaTeX-OCRpix2tex: Using a ViT to convert images of equations into LaTeX code.
Stars: ✭ 1,566 (+679.1%)
Mutual labels: transformer, vision-transformer
visualizationa collection of visualization function
Stars: ✭ 189 (-5.97%)
Mutual labels: transformer, vision-transformer
VT-UNet[MICCAI2022] This is an official PyTorch implementation for A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation
Stars: ✭ 151 (-24.88%)
Mutual labels: transformer, vision-transformer
GhostnetCV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.
Stars: ✭ 1,744 (+767.66%)
Mutual labels: transformer, vision-transformer
libaiLiBai(李白): A Toolbox for Large-Scale Distributed Parallel Training
Stars: ✭ 284 (+41.29%)
Mutual labels: transformer, vision-transformer
ViTs-vs-CNNs[NeurIPS 2021]: Are Transformers More Robust Than CNNs? (Pytorch implementation & checkpoints)
Stars: ✭ 145 (-27.86%)
Mutual labels: transformer
kaggle-champsCode for the CHAMPS Predicting Molecular Properties Kaggle competition
Stars: ✭ 49 (-75.62%)
Mutual labels: transformer
R-MeNTransformer-based Memory Networks for Knowledge Graph Embeddings (ACL 2020) (Pytorch and Tensorflow)
Stars: ✭ 74 (-63.18%)
Mutual labels: transformer
TokenLabelingPytorch implementation of "All Tokens Matter: Token Labeling for Training Better Vision Transformers"
Stars: ✭ 385 (+91.54%)
Mutual labels: transformer
capeContinuous Augmented Positional Embeddings (CAPE) implementation for PyTorch
Stars: ✭ 29 (-85.57%)
Mutual labels: transformer
Long-short Transformer substitutes the full self attention of the original Transformer models with an efficient attention that considers both long-range and short-term correlations. Each query attends to tokens from the segment-wise sliding window to capture short-term correlations, and the dynamically projected features to capture long-range correlations. To align the norms of the original and projected feature vectors and improve the efficacy of the aggregation, we normalize the original and project feature vectors with two sets of Layer Normalizations.