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sr-tod

This is the official code for the paper Visible and Clear: Finding Tiny Objects in Difference Map.

https://github.com/hiyuur/sr-tod

Science Score: 54.0%

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    CITATION.cff file
    Found CITATION.cff file
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    Links to: arxiv.org
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    Scientific vocabulary similarity
    Low similarity (8.0%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

This is the official code for the paper Visible and Clear: Finding Tiny Objects in Difference Map.

Basic Info
  • Host: GitHub
  • Owner: Hiyuur
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 21.1 MB
Statistics
  • Stars: 23
  • Watchers: 1
  • Forks: 4
  • Open Issues: 5
  • Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

SR-TOD

Introduce

This is the official code for the paper Visible and Clear: Finding Tiny Objects in Difference Map.

The link to the paper is https://arxiv.org/abs/2405.11276.

This project is built based on mmdetection 3.1 and mmcv 2.0.1.

NOTE: Our paper has been accepted by ECCV 2024.

Environment

pytorch 1.12.0

torchvision 0.13.0

mmdetection 3.1

mmcv 2.0.1

The installation and usage of mmdetection can be referred to at the following link: https://mmdetection.readthedocs.io/en/latest/get_started.html.

To use the AI-TOD evaluation metrics, you need to download aitodpycocotools. You can install it using the following command:

shell pip install "git+https://github.com/jwwangchn/cocoapi-aitod.git#subdirectory=aitodpycocotools"

For other environment requirements, please refer to mmdetection.

Training and Test

The training and test commands can also be referenced from mmdetection.

1 gpu:

shell python tools/train.py ./srtod_project/srtod_cascade_rcnn/config/srtod-cascade-rcnn_r50_fpn_1x_coco.py shell python tools/test.py ./srtod_project/srtod_cascade_rcnn/config/srtod-cascade-rcnn_r50_fpn_1x_coco.py your_model.pth

If you need to use more GPUs, you should use ./tools/dist_train.sh instead of tools/train.py.

DroneSwarms

If you want to access the DroneSwarms dataset, please visit the following link：DroneSwarms

Reference

shell @article{cao2024visible, title={Visible and Clear: Finding Tiny Objects in Difference Map}, author={Cao, Bing and Yao, Haiyu and Zhu, Pengfei and Hu, Qinghua}, journal={arXiv preprint arXiv:2405.11276}, year={2024} }

Owner

  • Login: Hiyuur
  • 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

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  • Issues event: 7
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Last synced: 6 months ago

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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
mmdet.egg-info/requires.txt pypi
  • asynctest *
  • cityscapesscripts *
  • codecov *
  • cython *
  • flake8 *
  • imagecorruptions *
  • instaboostfast *
  • interrogate *
  • isort ==4.3.21
  • kwarray *
  • matplotlib *
  • memory_profiler *
  • mmcv <2.1.0,>=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 *
  • transformers *
  • ubelt *
  • xdoctest >=0.10.0
  • yapf *
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,<2.1.0
  • mmengine >=0.7.1,<1.0.0
requirements/multimodal.txt pypi
  • nltk *
  • pycocoevalcap *
  • transformers *
requirements/optional.txt pypi
  • cityscapesscripts *
  • imagecorruptions *
  • scikit-learn *
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc4,<2.1.0
  • 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 *
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