A pytorch implementation for paper "Feature Learning based Deep Supervised Hashing with Pairwise Labels"
Environment: python 3
Requirements:
pytorch
torchvisionAs pytorch doesn't provide pretrained VGG-F model, unlike original DPSH paper, we use pretrained Alexnet or pretrained VGG-11 for feature learning part instead of pretrained VGG-F.
Following DPSH MatConvNet source code, we can obtain cifar-10.mat. To prepare data for pytorch version DPSH, run script ./data/CIFAR-10/SaveFig.m to save image files.
python DPSH_CIFAR_10_demo.py| Net Structure | PlatForm | Code Length | |||
| 12 bits | 24 bits | 32 bits | 48 bits | ||
| VGG-F | MatConvNet | 0.713 | 0.727 | 0.744 | 0.757 |
| Alexnet | Pytorch | 0.7505 | 0.7724 | 0.7758 | 0.7828 |
| VGG-11 | Pytorch | 0.7655 | 0.8042 | 0.8070 | 0.8108 |
