WangHaiXu1's starred repositories
segmentation_models.pytorch
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
SegLossOdyssey
A collection of loss functions for medical image segmentation
segment-anything-2
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
PythonRobotics
Python sample codes for robotics algorithms.
LeetCodeAnimation
Demonstrate all the questions on LeetCode in the form of animation.（用动画的形式呈现解LeetCode题目的思路）
problem-solving-with-algorithms-and-data-structures-using-python
Code and exercises from Problem and Solving with Algorithms and Data Structures
pytorch-styleguide
An unofficial styleguide and best practices summary for PyTorch
pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
pytorch-cnn-visualizations
Pytorch implementation of convolutional neural network visualization techniques
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
Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
PyTorch_Tutorial
《Pytorch模型训练实用教程》中配套代码
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Transformer-Explainability
[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
sota-data-augmentation-and-optimizers
This repository contains some of the latest data augmentation techniques and optimizers for image classification using pytorch and the CIFAR10 dataset
ISDA-for-Deep-Networks
An efficient implicit semantic augmentation method, complementary to existing non-semantic techniques.
CloserLookFewShot
source code to ICLR'19, 'A Closer Look at Few-shot Classification'
few-shot-meta-baseline
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, in ICCV 2021
Distilling-the-knowledge-in-neural-network
Teaches a student network from the knowledge obtained via training of a larger teacher network
ModelDistillation-PyTorch
PyTorch implementation of "Distilling the Knowledge in a Neural Network" for model compression
slimmable_networks
Slimmable Networks, AutoSlim, and Beyond, ICLR 2019, and ICCV 2019
texture-vs-shape
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" (ICLR 2019 Oral)