This is a Python toolbox that implements the training and testing of the approach described in our papers:
Fine-tuning CNN Image Retrieval with No Human Annotation,
Radenović F., Tolias G., Chum O.,
TPAMI 2018 [arXiv]
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples,
Radenović F., Tolias G., Chum O.,
ECCV 2016 [arXiv]
This code implements:
- Training (fine-tuning) CNN for image retrieval
- Learning supervised whitening for CNN image representations
- Testing CNN image retrieval on Oxford5k and Paris6k datasets
In order to run this toolbox you will need:
- Python3 (tested with Python 3.5.3 on Debian 8.1)
- PyTorch deep learning framework (tested with version 0.3.0.post4)
- All the rest (data + networks) is automatically downloaded with our scripts
Navigate (cd) to the root of the toolbox [YOUR_CIRTORCH_ROOT].
Example training script is located in YOUR_CIRTORCH_ROOT/cirtorch/examples/train.py
python3 -m cirtorch.examples.train.py [-h] [--training-dataset DATASET] [--no-val]
[--test-datasets DATASETS] [--test-whiten DATASET]
[--arch ARCH] [--pool POOL] [--whitening] [--not-pretrained]
[--loss LOSS] [--loss-margin LM] [--image-size N]
[--neg-num N] [--query-size N] [--pool-size N] [--gpu-id N]
[--workers N] [--epochs N] [--batch-size N]
[--optimizer OPTIMIZER] [--lr LR] [--momentum M]
[--weight-decay W] [--print-freq N] [--resume FILENAME]
DIR
For detailed explanation of the options run:
python3 -m cirtorch.examples.train.py -h
For example, to train our best network described in the TPAMI 2018 paper run the following command. After each epoch, the fine-tuned network will be tested on the revisited Oxford and Paris benchmarks:
python3 -m cirtorch.examples.train YOUR_EXPORT_DIR --gpu-id '0' --training-dataset 'retrieval-SfM-120k'
--test-datasets 'roxford5k,rparis6k' --arch 'resnet101' --pool 'gem' --loss 'contrastive'
--loss-margin 0.85 --optimizer 'adam' --lr 1e-6 --neg-num 5 --query-size=2000
--pool-size=20000 --batch-size 5 --image-size 362
Networks can be evaluated with learned whitening after each epoch. To achieve this run the following command. Note that this will significantly slow down the entire training procedure, and you can evaluate networks with learned whitening later on using the example test script.
python3 -m cirtorch.examples.train YOUR_EXPORT_DIR --gpu-id '0' --training-dataset 'retrieval-SfM-120k'
--test-datasets 'roxford5k,rparis6k' --test-whiten 'retrieval-SfM-30k'
--arch 'resnet101' --pool 'gem' --loss 'contrastive' --loss-margin 0.85
--optimizer 'adam' --lr 1e-6 --neg-num 5 --query-size=2000 --pool-size=20000
--batch-size 5 --image-size 362
Note: Data and networks used for training and testing are automatically downloaded when using the example script.
Example testing script is located in YOUR_CIRTORCH_ROOT/cirtorch/examples/test.py
python3 -m cirtorch.examples.test.py [-h] (--network-path NETWORK | --network-offtheshelf NETWORK)
[--datasets DATASETS] [--image-size N] [--multiscale]
[--whitening WHITENING] [--gpu-id N]
For detailed explanation of the options run:
python3 -m cirtorch.examples.test.py -h
We provide the pretrained networks trained using the same parameters as in our TPAMI 2018 paper, with precomputed whitening. To evaluate them run:
python3 -m cirtorch.examples.test --gpu-id '0' --network-path 'retrievalSfM120k-resnet101-gem'
--datasets 'oxford5k,paris6k,roxford5k,rparis6k'
--whitening 'retrieval-SfM-120k' --multiscale
or
python3 -m cirtorch.examples.test --gpu-id '0' --network-path 'retrievalSfM120k-vgg16-gem'
--datasets 'oxford5k,paris6k,roxford5k,rparis6k'
--whitening 'retrieval-SfM-120k' --multiscale
Performance comparison with the networks used in the paper, trained with our CNN Image Retrieval in MatConvNet:
| Model | Oxford | Paris | ROxf (M) | RPar (M) | ROxf (H) | RPar (H) |
|---|---|---|---|---|---|---|
| VGG16-GeM (MatConvNet) | 87.9 | 87.7 | 61.9 | 69.3 | 33.7 | 44.3 |
| VGG16-GeM (PyTorch) | 87.2 | 87.8 | 60.5 | 69.3 | 32.4 | 44.3 |
| ResNet101-GeM (MatConvNet) | 87.8 | 92.7 | 64.7 | 77.2 | 38.5 | 56.3 |
| ResNet101-GeM (PyTorch) | 88.2 | 92.5 | 65.3 | 76.6 | 40.0 | 55.2 |
To evaluate your trained network using single scale and without learning whitening:
python3 -m cirtorch.examples.test --gpu-id '0' --network-path YOUR_NETWORK_PATH
--datasets 'oxford5k,paris6k,roxford5k,rparis6k'
To evaluate trained network using multi scale evaluation and with learned whitening as post-processing:
python3 -m cirtorch.examples.test --gpu-id '0' --network-path YOUR_NETWORK_PATH
--datasets 'oxford5k,paris6k,roxford5k,rparis6k'
--whitening 'retrieval-SfM-120k' --multiscale
Off-the-shelf networks can be evaluated as well, for example:
python3 -m cirtorch.examples.test --gpu-id '0' --network-offtheshelf 'resnet101-gem'
--datasets 'oxford5k,paris6k,roxford5k,rparis6k'
--whitening 'retrieval-SfM-120k' --multiscale
Note: Data used for testing are automatically downloaded when using the example script.
@article{RTC18,
title = {Fine-tuning {CNN} Image Retrieval with No Human Annotation},
author = {Radenovi{\'c}, F. and Tolias, G. and Chum, O.}
journal = {TPAMI},
year = {2018}
}
@inproceedings{RTC16,
title = {{CNN} Image Retrieval Learns from {BoW}: Unsupervised Fine-Tuning with Hard Examples},
author = {Radenovi{\'c}, F. and Tolias, G. and Chum, O.},
booktitle = {ECCV},
year = {2016}
}
@inproceedings{RITAC18,
author = {Radenovi{\'c}, F. and Iscen, A. and Tolias, G. and Avrithis, Y. and Chum, O.},
title = {Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking},
booktitle = {CVPR},
year = {2018}
}