Deep Supervised Discrete Hashing

REQUIREMENTS

pip install -r requirements.txt

1. pytorch
2. loguru

DATASETS

1. CIFAR-10
2. NUS-WIDE Password: uhr3
3. Imagenet100 Password: ynwf

USAGE

usage: run.py [-h] [--dataset DATASET] [--root ROOT] [--batch-size BATCH_SIZE]
              [--arch ARCH] [--lr LR] [--code-length CODE_LENGTH]
              [--max-iter MAX_ITER] [--num-query NUM_QUERY]
              [--num-train NUM_TRAIN] [--num-workers NUM_WORKERS]
              [--topk TOPK] [--gpu GPU] [--mu MU] [--nu NU] [--eta ETA]
              [--evaluate-interval EVALUATE_INTERVAL]

DSDH_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: 128)
  --arch ARCH           CNN model name.(default: alexnet)
  --lr LR               Learning rate.(default: 1e-5)
  --code-length CODE_LENGTH
                        Binary hash code length.(default: 12,24,32,48)
  --max-iter MAX_ITER   Number of iterations.(default: 150)
  --num-query NUM_QUERY
                        Number of query data points.(default: 1000)
  --num-train NUM_TRAIN
                        Number of training data points.(default: 5000)
  --num-workers NUM_WORKERS
                        Number of loading data threads.(default: 6)
  --topk TOPK           Calculate map of top k.(default: all)
  --gpu GPU             Using gpu.(default: False)
  --mu MU               Hyper-parameter.(default: 1e-2)
  --nu NU               Hyper-parameter.(default: 1)
  --eta ETA             Hyper-parameter.(default: 1e-2)
  --evaluate-interval EVALUATE_INTERVAL
                        Evaluation interval.(default: 10)

EXPERIMENTS

CNN model: Alexnet. Compute mean average precision(MAP).

cifar10: 1000 query images, 5000 training images.

nus-wide-tc21: 21 classes, 2100 query images, 10500 training images.

Imagenet-tc100: 100 classes, 5000 query images, 10000 training images. mu=5, nu=5, eta=2.

bits	12	16	24	32	48	64	128
cifar10@ALL	0.6994	0.7052	0.7204	0.7267	0.7345	0.7466	0.7493
nus-wide-tc21@5000	0.8052	0.8155	0.8317	0.8332	0.8437	0.8478	0.8544
imagenet-tc100@1000	0.1622	0.1793	0.2356	0.2808	0.3350	0.3753	0.4274