itq_pytorch's Introduction

A pytorch implementation for paper "Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-scale Image Retrieval" TPAMI-2013

REQUIREMENTS

pip install -r requirements.txt

pytorch >= 1.0
loguru

DATASETS

cifar10-gist.mat password: umb6
cifar-10_alexnet.t password: f1b7
nus-wide-tc21_alexnet.t password: vfeu
imagenet-tc100_alexnet.t password: 6w5i

USAGE

usage: run.py [-h] [--dataset DATASET] [--root ROOT]
              [--code-length CODE_LENGTH] [--max-iter MAX_ITER] [--topk TOPK]
              [--gpu GPU]

ITQ_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.(default:
                        8,16,24,32,48,64,96,128)
  --max-iter MAX_ITER   Number of iterations.(default: 3)
  --topk TOPK           Calculate map of top k.(default: ALL)
  --gpu GPU             Using gpu.(default: False)

EXPERIMENTS

cifar10-gist dataset. Gist features, 1000 query images, 5000 training images, MAP@ALL.

cifar-10-alexnet dataset. Alexnet features, 1000 query images, 5000 training images, MAP@ALL.

nus-wide-tc21-alexnet dataset. Alexnet features, top 21 classes, 2100 query images, 10500 training images, MAP@5000.

imagenet-tc100-alexnet dataset. Alexnet features, top 100 classes, 5000 query images, 10000 training images, MAP@1000.

Bits	8	16	24	32	48	64	96	128
cifar10-gist@ALL	0.1484	0.1584	0.1613	0.1632	0.1672	0.1688	0.1726	0.1749
cifar10-alexnet@ALL	0.2000	0.2175	0.2215	0.2308	0.2386	0.2490	0.2551	0.2623
nus-wide-tc21-alexnet@5000	0.6423	0.6878	0.7016	0.7186	0.7280	0.7389	0.7500	0.7539
imagenet-tc100-alexnet@1000	0.1617	0.2369	0.2732	0.3296	0.3751	0.4076	0.4418	0.4554

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