Wanggcong's repositories
Deep-growing-learning
If you use these codes, please kindly cite our ICCV2017 paper: \bibitem{WangICCV2017} G.~Wang, X.~Xie, J.~Lai and J.~Zhuo, ``Deep Growing Learning," in ICCV, pp.2812-2820, 2017.
Kalman-Normalization
Code of "Batch Kalman Normalization: Towards Training Deep Neural Networks with Micro-Batches"
beyond-part-models
PCB of paper: Beyond Part Models: Person Retrieval with Refined Part Pooling, using Pytorch
expanded-cross-neighborhood
Expanded Cross Neighborhood distance based Re-ranking (ECN)
faster-rcnn.pytorch
A faster pytorch implementation of faster r-cnn
faster_rcnn_pytorch
Faster RCNN with PyTorch
lsoftmax-pytorch
PyTorch implementation Large-Margin Softmax (L-Softmax) loss
person-re-ranking
Person Re-ranking (CVPR 2017)
person-reid-benchmark
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
person-reid-triplet-loss-baseline
Rank-1 89% (Single Query) on Market1501 with raw triplet loss, In Defense of the Triplet Loss for Person Re-Identification, using Pytorch
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.
pose-sensitive-embedding
Pose Sensitive Embedding for Person Re-Identification (PSE)
pytorch-beginner
pytorch tutorial for beginners
pytorch-classification
Classification with PyTorch.
pytorch-groupnorm
Group Normalization in PyTorch
simple-faster-rcnn-pytorch
A simplified implemention of Faster R-CNN that replicate performance from origin paper
triplet-reid
Code for reproducing the results of our "In Defense of the Triplet Loss for Person Re-Identification" paper.
MGN-pytorch
Reproduction of paper: Learning Discriminative Features with Multiple Granularities for Person Re-Identification
OpenGL-Examples
A collection of simple single file OpenGL examples