zhouchuangchuang / cgd

A PyTorch implementation of CGD based on the paper "Combination of Multiple Global Descriptors for Image Retrieval"

CGD

A PyTorch implementation of CGD based on the paper Combination of Multiple Global Descriptors for Image Retrieval.

Requirements

• Anaconda
• PyTorch

conda install pytorch torchvision cudatoolkit=10.0 -c pytorch

• thop

pip install thop

Datasets

CARS196, CUB200-2011, Standard Online Products and In-shop Clothes are used in this repo.

You should download these datasets by yourself, and extract them into ${data_path} directory, make sure the dir names are car, cub, sop and isc. Then run data_utils.py to preprocess them.

Usage

Train CGD

python train.py --feature_dim 512 --gd_config SM
optional arguments:
--data_path                   datasets path [default value is '/home/data']
--data_name                   dataset name [default value is 'car'](choices=['car', 'cub', 'sop', 'isc'])
--crop_type                   crop data or not, it only works for car or cub dataset [default value is 'uncropped'](choices=['uncropped', 'cropped'])
--backbone_type               backbone network type [default value is 'resnet50'](choices=['resnet50', 'resnext50'])
--gd_config                   global descriptors config [default value is 'SG'](choices=['S', 'M', 'G', 'SM', 'MS', 'SG', 'GS', 'MG', 'GM', 'SMG', 'MSG', 'GSM'])
--feature_dim                 feature dim [default value is 1536]
--smoothing                   smoothing value for label smoothing [default value is 0.1]
--temperature                 temperature scaling used in softmax cross-entropy loss [default value is 0.5]
--margin                      margin of m for triplet loss [default value is 0.1]
--recalls                     selected recall [default value is '1,2,4,8']
--batch_size                  train batch size [default value is 128]
--num_epochs                  train epoch number [default value is 20]

Test CGD

python test.py --retrieval_num 10
optional arguments:
--query_img_name              query image name [default value is '/home/data/car/uncropped/008055.jpg']
--data_base                   queried database [default value is 'car_uncropped_resnet50_SG_1536_0.1_0.5_0.1_128_data_base.pth']
--retrieval_num               retrieval number [default value is 8]

Benchmarks

The models are trained on one NVIDIA Tesla V100 (32G) GPU with 20 epochs, the learning rate is decayed by 10 on 12th and 16th epoch.

Model Parameters and FLOPs (Params | FLOPs)

Backbone	CARS196	CUB200	SOP	In-shop
ResNet50	26.86M | 10.64G	26.86M | 10.64G	49.85M | 10.69G	34.85M | 10.66G
ResNeXt50	26.33M | 10.84G	26.33M | 10.84G	49.32M | 10.89G	34.32M | 10.86G

CARS196 (Uncropped | Cropped)

Backbone	R@1	R@2	R@4	R@8	Download Link
ResNet50(SG)	86.4% | 92.4%	92.1% | 96.1%	95.6% | 97.8%	97.5% | 98.7%	r3sn | sf5s
ResNeXt50(SG)	86.4% | 91.7%	92.0% | 95.4%	95.4% | 97.3%	97.6% | 98.6%	dsdx | fh72

CUB200 (Uncropped | Cropped)

Backbone	R@1	R@2	R@4	R@8	Download Link
ResNet50(MG)	66.0% | 73.9%	76.4% | 83.1%	84.8% | 89.6%	90.7% | 94.0%	2cfi | pi4q
ResNeXt50(MG)	66.1% | 73.7%	76.3% | 82.6%	84.0% | 89.0%	90.1% | 93.3%	nm9h | 6mkf

SOP

Backbone	R@1	R@10	R@100	R@1000	Download Link
ResNet50(SG)	79.3%	90.6%	95.8%	98.6%	qgsn
ResNeXt50(SG)	71.0%	85.3%	93.5%	97.9%	uexd

In-shop

Backbone	R@1	R@10	R@20	R@30	R@40	R@50	Download Link
ResNet50(GS)	83.6%	95.7%	97.1%	97.7%	98.1%	98.4%	8jmp
ResNeXt50(GS)	85.0%	96.1%	97.3%	97.9%	98.2%	98.4%	wdq5

Results

CAR/CUB (Uncropped | Cropped)

SOP/ISC