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.

1. Install the requirements (faiss-cpu for cpu-only setup)

pip3 install pyaml numpy faiss-gpu

2. Build C library for your Python version

python3 setup.py build_ext --inplace
rm -r build

3. Download cirtorch and add it to your PYTHONPATH

wget "https://github.com/filipradenovic/cnnimageretrieval-pytorch/archive/master.zip"
unzip master.zip
rm master.zip
export PYTHONPATH=${PYTHONPATH}:$(realpath cnnimageretrieval-pytorch-master)

4. Run examples/demo_how.py giving it any .yaml parameter file from examples/params/*.yml

Reproducing results from Table 2.

• R18_how (n = 1000):   examples/demo_how.py eccv20_how_r18_1000   ROxf (M): 75.1, RPar (M): 79.4
• -R50_how (n = 1000):   examples/demo_how.py eccv20_how_r50-_1000   ROxf (M): 78.3, RPar (M): 80.1
• -R50_how (n = 2000):   examples/demo_how.py eccv20_how_r50-_2000   ROxf (M): 79.4, RPar (M): 81.6