LWCHN's repositories
CellarV
Cellar is an easy to use, interactive, and comprehensive software tool for the assignment of cell types in single-cell studies.
Cerebro
Visualization of scRNA-seq data.
ChatGPT
🔮 ChatGPT Desktop Application (Mac, Windows and Linux)
cirrocumulus
Single-cell visualization
CTEC
CTEC single-cell RNA-seq ensemble clustering
DeepClustering
Methods and Implements of Deep Clustering
docker-pytorch
A Docker image for PyTorch
Fruit-Images-Dataset
Fruits-360: A dataset of images containing fruits and vegetables
learn2learn--resnet12
A PyTorch Library for Meta-learning Research
machine-learning-notes
Collection of useful machine learning codes and snippets (originally intended for my personal use)
MAE-pytorch
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners
MulimgViewer
MulimgViewer is a multi-image viewer that can open multiple images in one interface, which is convenient for image comparison and image stitching.
ORB_SLAM3_detailed_comments
Detailed comments for ORB-SLAM3
pyod
(JMLR'19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
Retail-Store-Item-Detection-using-YOLOv5
In this repository, I present a retail store item detector using YOLOv5
SAFEclustering
SAFE (Single-cell Aggregated clustering From Ensemble): Cluster ensemble for single-cell RNA-seq data
scanpy-tutorials
Scanpy Tutorials.
Supply-Chain-Management-Machine-Learning
Predicting Global Supply Chain Outcomes for Essential HIV Medicines using Machine Learning Techniques
TorchSemiSeg
[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
Vision-Transformer
Image classification using Vision Transformer PyTorch
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
ViT-pytorch-learn1
Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)
yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite