This is the code repository for our ICCV 2019 paper Metric Learning with HORDE: High-Order Regularizer for Deep Embedding.
- Python version 3.5 and higher
- Python packages from "requirements.txt"
- One of the used datasets (CUB, CARS, Stanford Online Products or In-Shop Clothes Retrieval)
The datasets should simply be extracted and put in some folder. You need to adapt the "config.ini" file according to your installation (dataset locations, temporary path, etc).
You also need one of the pre-trained backbones:
These files must be placed in the data folder.
You can adjust the training parameters from config.json (epoch, steps per epoch, image size, learning rate, etc).
pip install -r requirements.txt
Before running the default script, you must adjust config.ini according to your installation and config.json files for training parameters. With default parameters in config.json, the default script should give results around 60.0% Recall@1 on the Cub-200-2011 dataset:
sh run_cub.sh
To train a specific configuration, see the parameter help:
python3 train.py --help
For any questions, please feel free to reach
pierre.jacob@ensea.fr
As of now (September, 6th, 2019), the code supports 1.9.0 version Tensorflow for GPU usage and has not been tested with recent ones. For CPU usage, it still works with recent Tensorflow versions (tested with 1.14.0).
If you use this method or this code in your research, please cite as:
Pierre Jacob, David Picard, Aymeric Histace, Edouard Klein. Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings. In The IEEE International Conference on Computer Vision (ICCV), October 2019.
@InProceedings{JACOB_2019_ICCV,
title={Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings},
author={Jacob, Pierre and Picard, David and Histace, Aymeric and Klein, Edouard},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2019}
}
MIT License
Copyright (c) 2019 Pierre Jacob
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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