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

Owner

GitHub Repository

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

Related tags

Overview

Paper

Installation

Run Setting & Train & Inference

Hardware Requirements

Owner

A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

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