Awesome
HOW local descriptors
This is the official Python/PyTorch implementation of the HOW local descriptors from our ECCV 2020 paper:
@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"
}
Running the Code
- Install the cirtorch package (see cirtorch github for details)
# 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 the asmk package with dependencies (see asmk github for details)
# 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 pip3 requirements
pip3 install -r requirements.txt
- Run
examples/demo_how.py
with two arguments – mode (train
oreval
) and any.yaml
parameter file fromexamples/params/*/*.yml
Evaluating ECCV 2020 HOW models
Reproducing results from Table 2. with the publicly available models
- R18<sub>how</sub> (n = 1000):
examples/demo_how.py eval examples/params/eccv20/eval_how_r18_1000.yml -e official_how_r18_1000
ROxf (M): 75.1, RPar (M): 79.4 - -R50<sub>how</sub> (n = 1000):
examples/demo_how.py eval examples/params/eccv20/eval_how_r50-_1000.yml -e official_how_r50-_1000
ROxf (M): 78.3, RPar (M): 80.1 - -R50<sub>how</sub> (n = 2000):
examples/demo_how.py eval examples/params/eccv20/eval_how_r50-_2000.yml -e official_how_r50-_2000
ROxf (M): 79.4, RPar (M): 81.6
Training HOW models
- R18<sub>how</sub>:
- train:
examples/demo_how.py train examples/params/eccv20/train_how_r18.yml -e train_how_r18
- eval (n = 1000):
examples/demo_how.py eval examples/params/eccv20/eval_how_r18_1000.yml -ml train_how_r18
- train:
- -R50<sub>how</sub>:
- train:
examples/demo_how.py train examples/params/eccv20/eval_how_r50-.yml -e train_how_r50-
- eval (n = 1000):
examples/demo_how.py eval examples/params/eccv20/eval_how_r50-_1000.yml -ml train_how_r50-
- eval (n = 2000):
examples/demo_how.py eval examples/params/eccv20/eval_how_r50-_2000.yml -ml train_how_r50-
- train:
Dataset shuffling during the training is done according to the cirtorch package; randomness in the results is caused by cudnn and by kmeans for codebook creation during evaluation.