stones-zl's starred repositories
deep-efficient-person-reid
Experiment about Deep Person Re-identification with EfficientNet-v2
awesome-cs-books
计算机优质电子书整理，并且附带pdf下载链接，包括C，C++，Java，Python，Java，Linux，Go，数据结构与算法，人工智能，计算机基础，面试，设计模式，数据库，前端等
triplet-reid
Code for reproducing the results of our "In Defense of the Triplet Loss for Person Re-Identification" paper.
Awesome-person-re-identification
Awesome Person Re-identification
Person_reID_baseline_pytorch
:bouncing_ball_person: Pytorch ReID: A tiny, friendly, strong pytorch implement of person re-id / vehicle re-id baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial
deep-person-reid
Torchreid: Deep learning person re-identification in PyTorch.
UniPose
We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of- the-art results in single person pose detection for both sin- gle images and videos.
ThinkMatch
A research protocol for deep graph matching.
DeepLearning
Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现