PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
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Updated
May 15, 2024 - Python
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
A treasure chest for visual classification and recognition powered by PaddlePaddle
Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
face recognition training project(pytorch)
Optimize RandAugment with differentiable operations
Applying RandAugment on PointNet++
FastClassification is a tensorflow toolbox for class classification. It provides a training module with various backbones and training tricks towards state-of-the-art class classification.
[Re-implementation] FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Unofficial PyTorch Reimplementation of UniformAugment.
A simple template for classifying things
Full experimentation notebook for my Keras Example on using RandAugment.
Collection of deep learning modules
EfficientNet with Robust Training: MICCAI Skin Cancer Analysis Challenge
RandAugment with Keypoints Annotation Support.
This project includes multiple models, loss functions, optimizers and image augmentations for image classification task
SSL Methods integrated into the official implementation of the ECCV"22 Paper "CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition"
MobileNetV3 implementation using PyTorch
Prediction of MNIST data with only 100 labels
Image Classification Using Swin Transformer With RandAugment, CutMix, and MixUp
This repository contains the code and the report for the coursework of INFR11031 Advanced Vision, a postgraduate course offered at The University of Edinburgh. The task was to train on limited and improve the accuracy of the ResNet-50 classifier on a small subset of the ImageNet dataset containing 50K training images and 50K test images. Achieve…
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