A treasure chest for visual classification and recognition powered by PaddlePaddle

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

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.

HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

A PaddlePaddle version image model zoo.

2D Human Pose estimation using transformers. Implementation in Pytorch

(Unofficial) PyTorch implementation of Training Vision Transformers for Image Retrieval(El-Nouby, Alaaeldin, et al. 2021).

[CVPR 2024] Code for our Paper "DeiT-LT: Distillation Strikes Back for Vision Transformer training on Long-Tailed Datasets"

Pytorch implementation of some vision transformers, trained on CIFAR-10.

This repository holds the downstream task of Face Mask Classification performed on Self Currated Custom Dataset with various State of the Art deep learning models like ViT, BeIT, DeIT, LeViT, ConvNeXt, VGG16, EfficientNetV2, RegNet and MobileNetV3.

This is a warehouse for DeiT-pytorch-model, can be used to train your image dataset

Implementation of a Paper related to Vision Transformer

VisionTransformer for Tensorflow2

[CVPR'24] Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression

Image captioning with pretrained encoder on MSCOCO.

Implementing a C++ API for alcohol consumption detection using transfer learning based DeIT model

This is a warehouse for SBCFormer-pytorch-model, can be used to train your dataset

This is a warehouse for Agent-Attention-Models based on pytorch framework, can be used to train your image datasets.

"Neural Computing and Applications" Published Paper (2023)

The analysis of several vision-based transformers is the main emphasis of this project, which also analyzes their distinctive properties and evaluates how well they work using a common dataset. The study intends to obtain insights into the strengths and shortcomings of various transformer designs created for computer vision tasks.