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Use PyTorch to implement some classic frameworks

📘 The experiment tracker for foundation model training

Fast Differentiable Sorting and Ranking

Hierarchical Image Pyramid Transformer - CVPR 2022 (Oral)

(Pretrained weights provided) EfficientDet: Scalable and Efficient Object Detection implementation by Signatrix GmbH

[CVPR2024] OneFormer3D: One Transformer for Unified Point Cloud Segmentation

Training repository for OpenPose

PyTorch(1.6+) implementation of https://github.com/kang205/SASRec

Optimizing AlphaFold Training and Inference on GPU Clusters

PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

Keras.NET is a high-level neural networks API for C# and F#, with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano.

An implementation of 1D, 2D, and 3D positional encoding in Pytorch and TensorFlow

DiffSharp: Differentiable Functional Programming

Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019

Python library for analysing faces using PyTorch

⛔ [NOT MAINTAINED] An End-To-End Closed Domain Question Answering System.

High frequency trading (HFT) framework built for futures using machine learning and deep learning techniques

This is Pytorch re-implementation of our CVPR 2020 paper "Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation" (https://arxiv.org/abs/1911.10194)

🎉 PILOT: A Pre-trained Model-Based Continual Learning Toolbox

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

《动手学深度学习》习题解答，在线阅读地址如下：

Notes for Deep Learning Specialization Courses led by Andrew Ng.

PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning https://arxiv.org/abs/1611.09940

Neural Turing Machines (NTM) - PyTorch Implementation

[SuperGlue: Learning Feature Matching with Graph Neural Networks] This repo includes PyTorch code for training the SuperGlue matching network on top of SIFT keypoints and descriptors.

pytorch version of "End-to-end Recovery of Human Shape and Pose"

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (CVPR 2019 Oral)

Understanding Convolution for Semantic Segmentation

People Counting in Real-Time with an IP camera.

AI Image Signal Processing and Computational Photography. Official library for NTIRE (CVPR) and AIM (ICCV/ECCV) Challenges. You will find Learned ISPs, RAW Restoration-Upsampling-Reconstruction, Image Enhancement, Bokeh rendering and more!

臺灣大學 (NTU) 李宏毅教授「機器學習 (Machine Learning) 2021 Spring 」課程筆記

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

[ICLR 2020] Lite Transformer with Long-Short Range Attention

Unofficial pytorch implementation of 'Image Inpainting for Irregular Holes Using Partial Convolutions' [Liu+, ECCV2018]

Self-Supervised Learning of 3D Human Pose using Multi-view Geometry (CVPR2019)

基于法律裁判文书的事件抽取及其应用，包括数据的分词、词性标注、命名实体识别、事件要素抽取和判决结果预测等内容

Multi-person Human Pose Estimation with HRNet in Pytorch

A Benchmark of Text Classification in PyTorch

Flexible components pairing 🤗 Transformers with :zap: Pytorch Lightning

Everybody dance now reproduced in pytorch

A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

3D-aware GANs based on NeRF (arXiv).

Completed the CS231n 2017 spring assignments from Stanford university

Hierarchical Attention Networks for Document Classification in PyTorch

This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.

As the core component of the Huawei Ascend for PyTorch community, TorchNPU is a deep learning adapter plugin built by Ascend for PyTorch, enabling the PyTorch framework to directly invoke Ascend NPU and providing developers with the powerful compute capabilities of Ascend AI processors.

A subset of PyTorch's neural network modules, written in Python using OpenAI's Triton.

RNN-based generative models for speech.

Classify music genre from a 10 second sound stream using a Neural Network.