peiwang062's starred repositories
matting-survey
Deep Image Matting: A Comprehensive Survey
segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Patterns-Extreme-Temperatures
Investigated the global patterns of temperature extremes
Linear-Regression-Best-Index
Evaluated the performance of 3 billion+ band combinations of 9 vegetation index types by conducting regression analysis to determine the optimal chlorophyll index for chlorophyll content estimation.
TransformerEngine
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.
ExplainableML-Vision
This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.
swin-transformer-pytorch
Implementation of the Swin Transformer in PyTorch.
pytorch-fgvc-dataset
PyTorch custom dataset APIs -- CUB-200-2011, Stanford Dogs, Stanford Cars, FGVC Aircraft, NABirds, Tiny ImageNet, iNaturalist2017
mtt-distillation
Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"
fullgrad-saliency
Full-gradient saliency maps
ssl-evaluation
Evaluation of semi-supervised learning on challenging datasets
CenterTrack
Simultaneous object detection and tracking using center points.
nerf-pytorch
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.
FixMatch-pytorch
Unofficial Pytorch code for "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence" in NeurIPS'20. This repo contains reproduced checkpoints.
ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
MachineLearning_Python
机器学习算法python实现