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mmdetection

OpenMMLab Detection Toolbox and Benchmark

mmdetection-master

models

Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证的深度学习模型）

ms-tcn

nice-gan-pytorch

Official PyTorch implementation of NICE-GAN: Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation

ola

dfdff

pointrcnn

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019.

python

最良心的 Python 教程：

pytorch-faster-rcnn

pytorch based implementation faster rcnn

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-tutorial

PyTorch Tutorial for Deep Learning Researchers

pytorch-yolov3

Minimal PyTorch implementation of YOLOv3

pytorch_practice

这是我学习 PyTorch 的笔记对应的代码，点击查看 PyTorch 笔记在线电子书

qt-studentmanager

C++ Qt 基于数据库Mysql学生信息管理系统

referpy

查询论文的引用格式，支持批量查询

rgbt-ped-detection

KAIST Multispectral Pedestrian Detection Benchmark [CVPR '15]

scut_fir_pedestrian_dataset

A new benchmark dataset and baseline for on-road FIR pedestrian detection

sfa

Official Implementation of "Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers"

sparser-cnn

End-to-End Object Detection with Learnable Proposal

spider_python

python爬虫

towards-universal-object-detection

[CVPR 2019] Pytorch implementation for "Towards Universal Object Detection by Domain Attention."

transfer-learning

这是一个有关迁移学习的仓库，在这里可以看到迁移学习的各种用法。

transfer-learning-library

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

transferlearning

Everything about Transfer Learning and Domain Adaptation--迁移学习

transferlearning-tutorial

《迁移学习简明手册》LaTex源码

two

ufdn

A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation

very-deep-convolutional-networks-for-large-scale-image-recognition

white-box-cartoonization

Official tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations”

wtalc-pytorch

W-TALC: Weakly-supervised Temporal Activity Localization and Classification