zouliangyu's repositories
AlignedReID-Re-Production-Pytorch
Reproduce AlignedReID: Surpassing Human-Level Performance in Person Re-Identification, using Pytorch.
Augmentor
Image augmentation library in Python for machine learning.
caffe
Caffe: a fast open framework for deep learning.
caffe-windows
Configure Caffe in one hour for Windows users.
deep-residual-networks
Deep Residual Learning for Image Recognition
deeplearningbook-chinese
Deep Learning Book Chinese Translation
DenseNet
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).
DukeMTMC-attribute
23 hand-annotated attributes of Duke dataset
DukeMTMC-reID_evaluation
ICCV2017 The Person re-ID Evaluation Code for DukeMTMC-reID Dataset (Including Dataset Download)
fb.resnet.torch
Torch implementation of ResNet from http://arxiv.org/abs/1512.03385 and training scripts
insightface
Face Recognition Project on MXNet
Market-1501_Attribute
27 hand-annotated attributes of Market-1501
open-reid
Open source person re-identification library in python
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)
Pedestrian_Alignment
Pedestrian Alignment Network for Large-scale Person Re-identification
Person-reID_GAN
ICCV2017 Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
Person_reID_baseline_pytorch
Pytorch implement of Person re-identification baseline. We arrived Rank@1=88.24%, mAP=70.68% only with softmax loss. Re-ranking is added.
py-faster-rcnn
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Random-Erasing
Random Erasing Data Augmentation
siamese-fc
Arbitrary object tracking at 50-100 FPS with Fully Convolutional Siamese networks.
sphereface
Implementation for <SphereFace: Deep Hypersphere Embedding for Face Recognition> in CVPR'17.
triplet-reid
Code for reproducing the results of our "In Defense of the Triplet Loss for Person Re-Identification" paper.