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上海交通大学课程资料分享

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

Real-time wheelchair navigation with shared control using model predictive path integral (MPPI) controller

Shared control setup for R-net based wheelchairs

A curated list of resources relevant to LiDAR-Visual-Fusion-SLAM

📚 华章数学丛书高清扫描

EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD

CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification

This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

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A MATLAB program for Wi-Fi ray tracing simulation, which can be used in indoor positioning.

MambaOut: Do We Really Need Mamba for Vision? (CVPR 2025)

Publication-ready NN-architecture schematics.

Ensemble architecture of SegNet and UNet for Semantic Segmentation with keras

Official PyTorch implementation of SegFormer

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS. Also, including other vision transformers and CNNs for satellite, aerial image …

[WACV 2024] SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentation

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.

ICCV 2025 论文和开源项目合集

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

PyTorch Implementation of Focal Loss and Lovasz-Softmax Loss

Latex code for making neural networks diagrams