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Tensors and Dynamic neural networks in Python with strong GPU acceleration
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
A faster pytorch implementation of faster r-cnn
Official PyTorch implementation of StyleGAN3
Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]
[CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs
This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
My own leetcode solutions by python
A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation
[CVPR 2020] Interpreting the Latent Space of GANs for Semantic Face Editing
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
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
ML Collections is a library of Python Collections designed for ML use cases.
Vision Transformer (ViT) in PyTorch
A set of functions for supervised feature learning/classification of mental states from EEG based on "EEG images" idea.
Python script to stream EEG data from the muse 2016 headset
Library to compute surface distance based performance metrics for segmentation tasks.
Single Image Crowd Counting via MCNN (Unofficial Implementation)
Implementation of “DreamDiffusion: Generating High-Quality Images from Brain EEG Signals”
PyTorch implementations of several SOTA backbone deep neural networks (such as ResNet, ResNeXt, RegNet) on one-dimensional (1D) signal/time-series data.
[CVPR 2023] Collaborative Diffusion