Elias Ramzi ROADMAP Save
This repository contains the official implementation of the NeurIPS'21 paper, ROADMAP: Robust and Decomposable Average Precision for Image Retrieval.
Project README
Robust And Decomposable Average Precision for Image Retrieval (NeurIPS 2021)
This repository contains the source code for our ROADMAP paper (NeurIPS 2021).
Use ROADMAP
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
Datasets
We use the following datasets for our submission
- CUB-200-2011 (download link available on this website : http://www.vision.caltech.edu/visipedia/CUB-200.html)
- Stanford Online Products (you can download it here : https://cvgl.stanford.edu/projects/lifted_struct/)
- INaturalist-2018 (obtained from here https://github.com/visipedia/inat_comp/tree/master/2018#Data)
Run the code
SOP
The following command reproduce our results for Table 4.
CUDA_VISIBLE_DEVICES=0 python roadmap/single_experiment_runner.py \
'experience.experiment_name=sop_ROADMAP_${dataset.sampler.kwargs.batch_size}_sota' \
experience.seed=333 \
experience.max_iter=100 \
'experience.log_dir=${env:HOME}experiments/ROADMAP' \
optimizer=sop \
model=resnet \
transform=sop_big \
dataset=sop \
dataset.sampler.kwargs.batch_size=128 \
dataset.sampler.kwargs.batches_per_super_pair=10 \
loss=roadmap
With the transformer backbone :
CUDA_VISIBLE_DEVICES=0 python roadmap/single_experiment_runner.py \
'experience.experiment_name=sop_ROADMAP_${dataset.sampler.kwargs.batch_size}_DeiT' \
experience.seed=333 \
experience.max_iter=75 \
'experience.log_dir=${env:HOME}/experiments/ROADMAP' \
optimizer=sop_deit \
model=deit \
transform=sop \
dataset=sop \
dataset.sampler.kwargs.batch_size=128 \
dataset.sampler.kwargs.batches_per_super_pair=10 \
loss=roadmap
INaturalist
For ROADMAP sota results:
CUDA_VISIBLE_DEVICES='0,1,2' python roadmap/single_experiment_runner.py \
'experience.experiment_name=inat_ROADMAP_${dataset.sampler.kwargs.batch_size}_sota' \
experience.seed=333 \
experience.max_iter=90 \
'experience.log_dir=experiments/ROADMAP' \
optimizer=inaturalist \
model=resnet \
transform=inaturalist \
dataset=inaturalist \
dataset.sampler.kwargs.batch_size=384 \
loss=roadmap_inat
CUB-200-2011
For ROADMAP sota results:
CUDA_VISIBLE_DEVICES=0 python roadmap/single_experiment_runner.py \
'experience.experiment_name=cub_ROADMAP_${dataset.sampler.kwargs.batch_size}_sota' \
experience.seed=333 \
experience.max_iter=200 \
'experience.log_dir=${env:HOME}/experiments/ROADMAP' \
optimizer=cub \
model=resnet_max_ln \
transform=cub_big \
dataset=cub \
dataset.sampler.kwargs.batch_size=128 \
loss=roadmap
CUDA_VISIBLE_DEVICES=0 python roadmap/single_experiment_runner.py \
'experience.experiment_name=cub_ROADMAP_${dataset.sampler.kwargs.batch_size}_sota_DeiT' \
experience.seed=333 \
experience.max_iter=150 \
'experience.log_dir=${env:HOME}/experiments/ROADMAP' \
optimizer=cub_deit \
model=deit \
transform=cub \
dataset=cub \
dataset.sampler.kwargs.batch_size=128 \
loss=roadmap
The results are not exactly the same as my code changed a bit (for instance the random seed are not the same).
Contacts
If you have any questions don't hesitate to create an issue on this repository. Or send me an email at [email protected].
Don't hesitate to cite our work:
@inproceedings{
ramzi2021robust,
title={Robust and Decomposable Average Precision for Image Retrieval},
author={Elias Ramzi and Nicolas THOME and Cl{\'e}ment Rambour and Nicolas Audebert and Xavier Bitot},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021},
url={https://openreview.net/forum?id=VjQw3v3FpJx}
}
Resources
- Pytorch Metric Learning (PML): https://github.com/KevinMusgrave/pytorch-metric-learning
- SmoothAP: https://github.com/Andrew-Brown1/Smooth_AP
- Blackbox: https://github.com/martius-lab/blackbox-backprop
- FastAP: https://github.com/kunhe/FastAP-metric-learning
- SoftBinAP: https://github.com/naver/deep-image-retrieval
- timm: https://github.com/rwightman/pytorch-image-models
- PyTorch: https://github.com/pytorch/pytorch
- Hydra: https://github.com/facebookresearch/hydra
- Faiss: https://github.com/facebookresearch/faiss
- Ray: https://github.com/ray-project/ray
Open Source Agenda is not affiliated with "Elias Ramzi ROADMAP" Project. README Source: elias-ramzi/ROADMAP
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