Awesome
Python ASMK (Aggregated Selective Match Kernels)
This is a Python implementation of the ASMK approach published in ICCV 2013:
@InProceedings{TAJ13,
author = "Giorgos Tolias and Yannis Avrithis and Herv\'e J\'egou",
title = "To aggregate or not to aggregate: Selective match kernels for image search",
booktitle = "IEEE International Conference on Computer Vision",
year = "2013"
}
This package is provided to support image retrieval with local descriptors and to reproduce the results of our ECCV 2020 paper with HOW deep local descriptors:
@InProceedings{TJ20,
author = "Giorgos Tolias and Tomas Jenicek and Ond\v{r}ej Chum}",
title = "Learning and aggregating deep local descriptors for instance-level recognition",
booktitle = "European Conference on Computer Vision",
year = "2020"
}
There are minor differences compared to the original ASMK approach (ICCV'13) and implementation, which are described in our ECCV'20 paper. Using the provided package to run ASMK with other local descriptors is straightforward.
Running the Code
- Install the requirements (
faiss-cpu
for cpu-only setup)
pip3 install pyaml numpy faiss-gpu
- Build C library for your Python version
python3 setup.py build_ext --inplace
rm -r build
- Download
cirtorch
and add it to yourPYTHONPATH
wget "https://github.com/filipradenovic/cnnimageretrieval-pytorch/archive/master.zip"
unzip master.zip
rm master.zip
export PYTHONPATH=${PYTHONPATH}:$(realpath cnnimageretrieval-pytorch-master)
- Run
examples/demo_how.py
giving it any.yaml
parameter file fromexamples/params/*.yml
Reproducing ECCV 2020 results with HOW local descriptors
Reproducing results from Table 2.
- R18<sub>how</sub> (n = 1000):
examples/demo_how.py eccv20_how_r18_1000
ROxf (M): 75.1, RPar (M): 79.4 - -R50<sub>how</sub> (n = 1000):
examples/demo_how.py eccv20_how_r50-_1000
ROxf (M): 78.3, RPar (M): 80.1 - -R50<sub>how</sub> (n = 2000):
examples/demo_how.py eccv20_how_r50-_2000
ROxf (M): 79.4, RPar (M): 81.6