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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