Multi-Label Hashing for Dependency Relations among Multiple Objectives (DRMH)

The official implementation of Multi-Label Hashing for Dependency Relations among Multiple Objectives

Requirements

• Linux with Python >= 3.7
• PyTorch >= 1.7.0
• torchvision that matches the PyTorch installation
• CUDA 11.0

Getting Started

Train

The datasets files could be obtained from https://pan.baidu.com/s/10OBhx3FHf_RpK4rK_CACZQ, code:xuu7

Train on MIRFLICKR-25K, hash bit: 32bit

Trained model will be saved in 'weight/flickr/'

python train.py --dataset flickr --hash_code_length 32 --num_classes 38

Train on IAPRTC12, hash bits: 32bit

Trained model will be saved in 'weight/iaprtc/'

python train.py --dataset iaprtc --hash_code_length 32 --num_classes 22

Train on NUS-WIDE, hash bit: 32bit

Trained model will be saved in 'weight/nuswide/'

python train.py --dataset nuswide --hash_code_length 32 --num_classes 21

Train on LOCKED-BIKE, hash bit: 4bit

Trained model will be saved in 'weight/bikelock/'

python train.py --dataset bikelock --hash_code_length 4 --num_classes 4

Test

It will take a long time to generate hash codes for database, because of the large-scale data size for database

Test for MIRFLICKR-25K, hash bit: 32bit

python eval.py --dataset flickr --hash_code_length 32 --num_classes 38 --weight_pth 'your weight directory'

Test for IAPRTC12, hash bits: 32bit

python train.py --dataset iaprtc --hash_code_length 32 --num_classes 22 --weight_pth 'your weight directory'

Test for NUS-WIDE, hash bit: 32bit

python train.py --dataset nuswide --hash_code_length 32 --num_classes 21 --weight_pth 'your weight directory'

Test for LOCKED-BIKE, hash bit: 4bit

python train.py --dataset bikelock --hash_code_length 4 --num_classes 4 --weight_pth 'your weight directory'