TreezzZ / ADSH_PyTorch

Source code for paper "Asymmetric Deep Supervised Hashing" on AAAI-2018

Asymmetric Deep Supervised Hashing

REQUIREMENTS

1. pytorch >= 1.0
2. loguru

DATASETS

1. CIFAR-10
2. NUS-WIDE Password: uhr3

USAGE

usage: run.py [-h] [--dataset DATASET] [--root ROOT] [--batch-size BATCH_SIZE]
              [--lr LR] [--code-length CODE_LENGTH] [--max-iter MAX_ITER]
              [--max-epoch MAX_EPOCH] [--num-query NUM_QUERY]
              [--num-train NUM_TRAIN] [--num-workers NUM_WORKERS]
              [--topk TOPK] [--gpu GPU] [--gamma GAMMA]

ADSH_PyTorch

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET     Dataset name.
  --root ROOT           Path of dataset
  --batch-size BATCH_SIZE
                        Batch size.(default: 64)
  --lr LR               Learning rate.(default: 1e-4)
  --code-length CODE_LENGTH
                        Binary hash code length.(default: 12)
  --max-iter MAX_ITER   Number of iterations.(default: 50)
  --max-epoch MAX_EPOCH
                        Number of epochs.(default: 3)
  --num-query NUM_QUERY
                        Number of query data points.(default: 1000)
  --num-train NUM_TRAIN
                        Number of training data points.(default: 2000)
  --num-workers NUM_WORKERS
                        Number of loading data threads.(default: 0)
  --topk TOPK           Calculate map of top k.(default: all)
  --gpu GPU             Using gpu.(default: False)
  --gamma GAMMA         Hyper-parameter.(default: 200)

EXPERIMENTS

cifar10: 1000 query images, 2000 sampling images.

nus-wide: Top 21 classes, 2100 query images, 2000 sampling images.

model: Alexnet

	12 bits	24 bits	32 bits	48 bits
cifar-10 MAP@ALL	0.9075	0.9047	0.9116	0.9045
nus-wide MAP@5000	0.8698	0.9022	0.9079	0.9133