Learning Super-Features for Image Retrieval
This repository contains the code for running our FIRe model presented in our ICLR'22 paper:
@inproceedings{superfeatures,
title={{Learning Super-Features for Image Retrieval}},
author={{Weinzaepfel, Philippe and Lucas, Thomas and Larlus, Diane and Kalantidis, Yannis}},
booktitle={{ICLR}},
year={2022}
}
License
The code is distributed under the CC BY-NC-SA 4.0 License. See LICENSE for more information. It is based on code from HOW, cirtorch and ASMK that are released under their own license, the MIT license.
Preparation
After cloning this repository, you must also have HOW, cirtorch and ASMK and have them in your PYTHONPATH.
- install HOW
git clone https://github.com/gtolias/how
export PYTHONPATH=${PYTHONPATH}:$(realpath how)
- install cirtorch
wget "https://github.com/filipradenovic/cnnimageretrieval-pytorch/archive/v1.2.zip"
unzip v1.2.zip
rm v1.2.zip
export PYTHONPATH=${PYTHONPATH}:$(realpath cnnimageretrieval-pytorch-1.2)
- install ASMK
git clone https://github.com/jenicek/asmk.git
pip3 install pyaml numpy faiss-gpu
cd asmk
python3 setup.py build_ext --inplace
rm -r build
cd ..
export PYTHONPATH=${PYTHONPATH}:$(realpath asmk)
- install dependencies by running:
pip3 install -r how/requirements.txt
- data/experiments folders
All data will be stored under a folder fire_data that will be created when running the code; similarly, results and models from all experiments will be stored under folder fire_experiments
Evaluating our ICLR'22 FIRe model
To evaluate on ROxford/RParis our model trained on SfM-120k, simply run
python evaluate.py eval_fire.yml
With the released model and the parameters found in eval_fire.yml, we obtain 90.3 on the validation set, 82.6 and 62.2 on ROxford medium and hard respectively, 85.2 and 70.0 on RParis medium and hard respectively.
Training a FIRe model
Simply run
python train.py train_fire.yml -e train_fire
All training outputs will be saved to fire_experiments/train_fire.
To evaluate the trained model that was saved in fire_experiments/train_fire, simply run:
python evaluate.py eval_fire.yml -e train_fire -ml train_fire
Pretrained models
For reproducibility, we provide the following model weights for the architecture we use in the paper (ResNet50 without the last block + LIT):
- Model pre-trained on ImageNet-1K (with Cross-Entropy, the pre-trained model we use for training FIRe) (link)
- Model trained on SfM-120k trained with FIRe (link)
They will be automatically downloaded when running the training / testing script.
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