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simlayout_retrieval

https://github.com/miridih-jjkim/simlayout_retrieval

Science Score: 44.0%

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    Low similarity (9.3%) to scientific vocabulary
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Repository

Basic Info
  • Host: GitHub
  • Owner: miridih-jjkim
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 18.8 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

SimLayout Retrieval

This project provides tools for extracting embeddings and calculating similarity scores between images using a pre-trained model.

Requirements

  • Python 3.x
  • PyTorch
  • Other dependencies as specified in requirements.txt

Installation

  1. Clone the repository: bash git clone https://github.com/miridih-jjkim/simlayout_retrieval.git cd simlayout_retrieval

  2. Install the required packages: bash pip install -r requirements.txt

Usage

Embedding Extraction

To extract embeddings from images, use the following command:

  • /data/decoreted/test: Path to the directory containing the images for which embeddings need to be extracted. Change it to your own directory.
  • /data/ckpt_codetr/epoch_16.pth: Path to the pre-trained model checkpoint. Do not change it untill the new model is released.
  • --out-dir /data/sample_results: Directory where the extracted embeddings will be saved.

bash CUDA_VISIBLE_DEVICES=0 python demo/inference_demo.py /data/decoreted/test /data/ckpt_codetr/epoch_16.pth --out-dir /data/sample_results

Similarity Calculation

To calculate similarity scores between images, run:

bash CUDA_VISIBLE_DEVICES=0 python demo/cal_sim_score.py

This script will compute similarity scores for a set of target images against all other images in the specified directory and save the results in JSON format.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • DreamSim for the pre-trained model and embedding extraction framework.

Owner

  • Login: miridih-jjkim
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMDetection Contributors"
title: "OpenMMLab Detection Toolbox and Benchmark"
date-released: 2018-08-22
url: "https://github.com/open-mmlab/mmdetection"
license: Apache-2.0

GitHub Events

Total
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  • Member event: 2
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Dependencies

docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve_cn/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
dino_requirements.txt pypi
  • cuml-cu11 *
  • fvcore *
  • iopath *
  • omegaconf *
  • submitit *
  • torch ==2.0.0
  • torchmetrics ==0.10.3
  • torchvision ==0.15.0
  • xformers ==0.0.18
mmdet.egg-info/requires.txt pypi
  • asynctest *
  • cityscapesscripts *
  • codecov *
  • cython *
  • emoji *
  • fairscale *
  • flake8 *
  • imagecorruptions *
  • instaboostfast *
  • interrogate *
  • isort ==4.3.21
  • jsonlines *
  • kwarray *
  • matplotlib *
  • memory_profiler *
  • mmcv <2.2.0,>=2.0.0rc4
  • mmengine <1.0.0,>=0.7.1
  • mmpretrain *
  • mmtrack *
  • motmetrics *
  • nltk *
  • numpy <1.24.0
  • numpy *
  • onnx ==1.7.0
  • onnxruntime >=1.8.0
  • parameterized *
  • prettytable *
  • protobuf <=3.20.1
  • psutil *
  • pycocoevalcap *
  • pycocotools *
  • pytest *
  • scikit-learn *
  • scipy *
  • seaborn *
  • shapely *
  • six *
  • terminaltables *
  • tqdm *
  • transformers *
  • ubelt *
  • xdoctest >=0.10.0
  • yapf *
new_requirements.txt pypi
  • miminstall *
  • miminstallmmdet *
  • miminstallmmengine *
  • pipinstallnumpy ==1.26.4
  • pipinstallopenmim *
  • pipinstalltorch ==2.1.2
  • pipinstalltorchvision ==0.16.2
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
  • urllib3 <2.0.0
requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
requirements/multimodal.txt pypi
  • fairscale *
  • jsonlines *
  • nltk *
  • pycocoevalcap *
  • transformers *
requirements/optional.txt pypi
  • cityscapesscripts *
  • emoji *
  • fairscale *
  • imagecorruptions *
  • scikit-learn *
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
  • scipy *
  • torch *
  • torchvision *
  • urllib3 <2.0.0
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • pycocotools *
  • scipy *
  • shapely *
  • six *
  • terminaltables *
  • tqdm *
requirements/tests.txt pypi
  • asynctest * test
  • cityscapesscripts * test
  • codecov * test
  • flake8 * test
  • imagecorruptions * test
  • instaboostfast * test
  • interrogate * test
  • isort ==4.3.21 test
  • kwarray * test
  • memory_profiler * test
  • nltk * test
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • parameterized * test
  • prettytable * test
  • protobuf <=3.20.1 test
  • psutil * test
  • pytest * test
  • transformers * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements/tracking.txt pypi
  • mmpretrain *
  • motmetrics *
  • numpy <1.24.0
  • scikit-learn *
  • seaborn *
requirements.txt pypi
setup.py pypi
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wandb/run-20241230_044224-lbcsp58u/files/requirements.txt pypi
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  • Pygments ==2.17.2
  • addict ==2.4.0
  • aliyun-python-sdk-core ==2.16.0
  • aliyun-python-sdk-kms ==2.16.5
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