pip install -r requirements.txt
- pytorch >= 1.0
- loguru
- CIFAR-10 Password: aemd
- NUS-WIDE Password: msfv
- Imagenet100 Password: xpab
usage: run.py [-h] [--dataset DATASET] [--root ROOT]
[--code-length CODE_LENGTH] [--arch ARCH]
[--batch-size BATCH_SIZE] [--lr LR] [--max-iter MAX_ITER]
[--num-workers NUM_WORKERS] [--topk TOPK] [--gpu GPU]
[--alpha ALPHA] [--seed SEED]
[--evaluate-interval EVALUATE_INTERVAL]
HashNet_PyTorch
optional arguments:
-h, --help show this help message and exit
--dataset DATASET Dataset name.
--root ROOT Path of dataset
--code-length CODE_LENGTH
Binary hash code length.
--arch ARCH CNN model name.(default: alexnet)
--batch-size BATCH_SIZE
Batch size.(default: 256)
--lr LR Learning rate.(default: 1e-5)
--max-iter MAX_ITER Number of iterations.(default: 300)
--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)
--alpha ALPHA Hyper-parameter.(default: 1)
--seed SEED Random seed.(default: 3367)
--evaluate-interval EVALUATE_INTERVAL
Evaluation interval.(default: 10)
CNN model: Alexnet.
cifar10: 1000 query images, 5000 training images, MAP@ALL.
nus-wide: Top 21 classes, 2100 query images, 10500 training images, MAP@5000.
imagenet100: Top 100 classes, 5000 query images, 10000 training images, MAP@1000.
| bits | 16 | 32 | 48 | 128 |
|---|---|---|---|---|
| cifar10@ALL | 0.7290 | 0.7528 | 0.7512 | 0.7579 |
| nus-wide-tc21@5000 | 0.7981 | 0.8200 | 0.8300 | 0.8424 |
| imagenet100@1000 | 0.3651 | 0.4629 | 0.5094 | 0.5787 |