Furong Xu's repositories
AlignedReID
AlignedReID
DukeMTMC-Pose
Pedestrian Pose Annotation for DukeMTMC-reID by machine (Python and Matlab API)
reid-strong-baseline
Bag of Tricks and A Strong Baseline for Deep Person Re-identification
CLUEDatasetSearch
搜索所有中文NLP数据集,附常用英文NLP数据集
CosFace_pytorch
Pytorch implementation of CosFace
deep-person-reid
Pytorch implementation of deep person re-identification models.
Deep_metric
Deep Metric Learning
DeepHash-pytorch
Implementation of Some Deep Hash Algorithms, Including DPSH、DSH、DHN、HashNet、DSDH、DTSH、DFH、GreedyHash、CSQ.
deeplab_v3
Tensorflow Implementation of the Semantic Segmentation DeepLab_V3 CNN
Flow-Guided-Feature-Aggregation
Flow-Guided Feature Aggregation for Video Object Detection
HRNet-Facial-Landmark-Detection
High-resolution representation learning (HRNets) for facial landmark detection
htmlnavbar
Practicing HTML/CSS Navbar
invisible-watermark
python library for invisible image watermark (blind image watermark)
Person_reID_baseline_pytorch
Pytorch implement of Person re-identification baseline. We arrived Rank@1=88.24%, mAP=70.68% only with softmax loss.
Pet-ReID-IMAG
CVPR2022 Biometrics Workshop Pet Biometric Challenge
pretrained-models.pytorch
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
PS-FCN_Poster_LaTex
LaTex Poster for PS-FCN (ECCV 2018)
pytorch-cnn-visualizations
Pytorch implementation of convolutional neural network visualization techniques
sphereface_pytorch
A PyTorch Implementation of SphereFace.
Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
TokenLabeling
Pytorch implementation of "All Tokens Matter: Token Labeling for Training Better Vision Transformers"
unilm
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
Watermark-JPEG-with-8x8DCT
JPEG digital watermark based on modified DCT mid-frequency coefficient.
Xception-PyTorch
A PyTorch implementation of Xception: Deep Learning with Depthwise Separable Convolutions