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course-ml2021_hung-yi-lee

Machine Learning 2021 Hung-yi Lee Homework

cpp_primer_practice

搞定C++:punch:。C++ Primer 中文版第5版学习仓库，包括笔记和课后练习答案。

cv_paperdaily

CV 论文笔记

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

interpretdl

InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库

lihang-code

《统计学习方法》的代码实现

models

Models and examples built with TensorFlow

oldvideorepair_paddlegan

OldVedioRepair, 使用插帧(DAIN), 上色(DeOldify), 超分(EDVR)这三个模型对该视频进行修复。

paddle

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

paddledetection

Object detection and instance segmentation toolkit based on PaddlePaddle.

paddlegan

PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, wav2lip, picture repair, image editing, photo2cartoon, image style transfer, and so on.

paddlenlp

NLP Core Library and Model Zoo based on PaddlePaddle 2.0

paddlepaperreproduction

Reproduction of Paper

parl

A high-performance distributed training framework for Reinforcement Learning

pedestrianposedetection

Pedestrian multi target tracking and attitude detection system based on PaddlePaddle

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytorch-cifar100

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

pytorch-handbook

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

pytorch_cookbook

qcnet

[CVPR 2023] Query-Centric Trajectory Prediction

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

rise

Detect model's attention

sort-algorithm

trajectory-cluster

Implement Trajectory Clustering: A Partition-and-Group Framework

transformer-mm-explainability

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

transition_attention_maps

Official code of Transition Attention Maps

xianrenyty.github.io

yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite