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.

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Overview

Paper

Installation

Run Setting & Train & Inference

Hardware Requirements

Owner

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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

[TPDS'21] COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

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Official Python implementation of the 'Sparse deconvolution'-v0.3.0

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This repository contains the code for "SBEVNet: End-to-End Deep Stereo Layout Estimation" paper by Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider

OBBDetection is a oriented object detection library, which is based on MMdetection.

Coursera - Quiz & Assignment of Coursera

STMTrack: Template-free Visual Tracking with Space-time Memory Networks

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