A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

Last update: Dec 13, 2022

Overview

Paper

Installation

git clone https://github.com/seungkee/2nd-place-solution-to-Facebook-Image-Similarity-Matching-Track.git
sudo docker pull pytorch/pytorch:1.7.1-cuda11.0-cudnn8-devel
sudo docker run --runtime=nvidia --rm -it --ipc=host --gpus all -v $pwd/2nd-place-solution-to-Facebook-Image-Similarity-Matching-Track:/submission pytorch/pytorch:1.7.1-cuda11.0-cudnn8-devel

Run Setting & Train & Inference

cd /submission
chmod +x run.sh
./run.sh

Hardware Requirements

Local Drive Capacity >= 4TB, A100 40GB GPU x 8

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Comments

Trained weights

Congratulations on the great results! The idea of reranking with classification is simple and powerful.

Could you publish pretrained weights? (aws, google drive, github releases?) This will make it much easier to reproduce results.

Thanks in advance!

opened by kilianovski 0

Owner

GitHub

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