xhl-video

xhl-video's Stars

• huggingface/pytorch-image-models

  The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

  Language:Python33.5k3199604.9k

• microsoft/Swin-Transformer

  This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

  Language:Python14.5k1243222.1k

• facebookresearch/dino

  PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

  Language:Python6.7k66248942

• google-research/big_vision

  Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.

  Language:Jupyter Notebook2.7k3972176

• pengzhiliang/MAE-pytorch

  Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

  Language:Python2.6k2497343

• ytongbai/LVM

  Language:Python1.8k1162459

• MCG-NJU/VideoMAE

  [NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

  Language:Python1.5k15125143

• Sense-X/UniFormer

  [ICLR2022] official implementation of UniFormer

  Language:Python84910133113

• Alibaba-MIIL/ImageNet21K

  Official Pytorch Implementation of: "ImageNet-21K Pretraining for the Masses"(NeurIPS, 2021) paper

  Language:Python754107171

• bytedance/ibot

  iBOT :robot:: Image BERT Pre-Training with Online Tokenizer (ICLR 2022)

  Language:Jupyter Notebook71453580

• facebookresearch/flip

  Official Open Source code for "Scaling Language-Image Pre-training via Masking"

  Language:Python4156216

• UCSC-VLAA/CLIPA

  [NeurIPS 2023] This repository includes the official implementation of our paper "An Inverse Scaling Law for CLIP Training"

  Language:Python311141214

• Beckschen/3D-TransUNet

  This is the official repository for the paper "3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers"

  Language:Python23733420

• ytongbai/ViTs-vs-CNNs

  [NeurIPS 2021]: Are Transformers More Robust Than CNNs? (Pytorch implementation & checkpoints)

  Language:Python17715910

• UCSC-VLAA/RobustCNN

  [ICLR 2023] This repository includes the official implementation our paper "Can CNNs Be More Robust Than Transformers?"

  Language:Python1433113

• UCSC-VLAA/DMAE

  [CVPR 2023] This repository includes the official implementation our paper "Masked Autoencoders Enable Efficient Knowledge Distillers"

  Language:Python104385

• amazon-science/exponential-moving-average-normalization

  PyTorch implementation of EMAN for self-supervised and semi-supervised learning: https://arxiv.org/abs/2101.08482

  Language:Python10310413

• OliverRensu/D-iGPT

  [ICML 2024] This repository includes the official implementation of our paper "Rejuvenating image-GPT as Strong Visual Representation Learners"

  Language:Python97308

• nazmul-karim170/UNICON-Noisy-Label

  [CVPR'22] Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning"

  Language:Python6021215

• UCSC-VLAA/EVP

  [TMLR'24] This repository includes the official implementation our paper "Unleashing the Power of Visual Prompting At the Pixel Level"

  Language:Python39104

• Andy1621/CT-Net

  [ICLR2021] official implementation of CT-Net

  Language:Python372210

• meijieru/fast_advprop

  [ICLR 2022]: Fast AdvProp

  Language:Python34510

• yuyinzhou/L2B

  This repository includes the official project of L2B, from our paper "Learning to Bootstrap for Combating Label Noise".

  Language:Python32353

• UCSC-VLAA/FedConv

  [TMLR'24] This repository includes the official implementation our paper "FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning"

  Language:Python25100

• UCSC-VLAA/Image-Pretraining-for-Video

  [ECCV 2022] This repository includes the official implementation our paper "In Defense of Image Pre-Training for Spatiotemporal Recognition".

  Language:Python19010