https://github.com/jeromerony/dml_cross_entropy
Code for the paper "A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses" (ECCV 2020 - Spotlight)
https://github.com/jeromerony/dml_cross_entropy
cross-entropy deep-learning metric-learning
Last synced: about 1 month ago
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Code for the paper "A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses" (ECCV 2020 - Spotlight)
- Host: GitHub
- URL: https://github.com/jeromerony/dml_cross_entropy
- Owner: jeromerony
- License: bsd-3-clause
- Created: 2020-03-06T22:12:40.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-25T21:45:50.000Z (over 2 years ago)
- Last Synced: 2024-08-04T03:11:59.620Z (10 months ago)
- Topics: cross-entropy, deep-learning, metric-learning
- Language: Python
- Homepage: https://arxiv.org/abs/2003.08983
- Size: 29.3 KB
- Stars: 164
- Watchers: 6
- Forks: 18
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Requirements for the experiments
- scikit-learn
- pytorch >= 1.4
- sacred >= 0.8
- tqdm
- visdom_logger https://github.com/luizgh/visdom_logger
- faiss https://github.com/facebookresearch/faiss
## Data management
For In-Shop, you need to manually download the data from https://drive.google.com/drive/folders/0B7EVK8r0v71pVDZFQXRsMDZCX1E (at least the `img.zip` and `list_eval_partition.txt`), put them in `data/InShop` and extract `img.zip`.
You can download and generate the `train.txt` and `test.txt` for every dataset using the `prepare_data.py` script with:
```bash
python prepare_data.py
```
This will download and prepare all the necessary data for _CUB200_, _Cars-196_ and _Stanford Online Products_.
## Usage
This repo uses `sacred` to manage the experiments.
To run an experiment (e.g. on CUB200):
```bash
python experiment.py with dataset.cub
```
You can add an observer to save the metrics and files related to the expriment by adding `-F result_dir`:
```bash
python experiment.py -F result_dir with dataset.cub
```
## Reproducing the results of the paper
CUB200
```bash
python experiment.py with dataset.cub model.resnet50 epochs=30 lr=0.02
```
CARS-196
```bash
python experiment.py with dataset.cars model.resnet50 epochs=100 lr=0.05 model.norm_layer=batch
```
Stanford Online Products
```bash
python experiment.py with dataset.sop model.resnet50 epochs=100 lr=0.003 momentum=0.99 nesterov=True model.norm_layer=batch
```
In-Shop
```bash
python experiment.py with dataset.inshop model.resnet50 epochs=100 lr=0.003 momentum=0.99 nesterov=True model.norm_layer=batch
```
## Citation
```bibtex
@inproceedings{boudiaf2020unifying,
title={A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses},
author={Boudiaf, Malik and Rony, J{\'e}r{\^o}me and Ziko, Imtiaz Masud and Granger, Eric and Pedersoli, Marco and Piantanida, Pablo and {Ben Ayed}, Ismail},
booktitle={European Conference on Computer Vision},
pages={548--564},
year={2020},
organization={Springer}
}
```