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