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rcod

https://github.com/zhimengxin/rcod

Science Score: 44.0%

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    Found CITATION.cff file
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    .zenodo.json file
    Found .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (13.2%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: zhimengXin
  • License: apache-2.0
  • Language: Python
  • Default Branch: master
  • Size: 12.1 MB
Statistics
  • Stars: 8
  • Watchers: 1
  • Forks: 1
  • Open Issues: 1
  • Releases: 0
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

Toward Realistic Camouflaged Object Detection: Benchmarks and Method

Dataset Link

Google

Baidu Extract Code: 93yd

| Datasets | Categories | Training Images | Test Images | | ---- | ---- | ---- | ---- | | COD10K-D | 68 | 6000 | 4000 | | NC4K-D | 37 | 2863 | 1227 | | CAMO-D | 43 | 744 | 497 |

Framework install

Our code is based on MMDetection. Here, for the convenience of readers, we have uploaded the full code of mmdetection and our code. If the relevant environment for mmdetection is configured on your server, you can download and use it directly. MMDetection is an open source object detection toolbox based on PyTorch. We adopt MMDetection as our baseline framework from MMdetection

Our environmental installation * Linux with Python >= 3.10 * conda create -n RCOD python==3.10 * conda activate RCOD * PyTorch >= 2.1.1 & torchvision that matches the PyTorch version. * Our CUDA is 11.8 * Install PyTorch 2.1.1 with CUDA 11.8 shell conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=11.8 -c pytorch -c nvidia * pip install mmcv>=2.2.0 * pip install -r requirements/build.txt * pip install -v -e .

Training on APG

  • We provide the config files of the three datasets together, thus the number of categories in the config file and the path of the dataset needed to be changed during training. Here, data modification includes: RCOD/mmdet/datasets/coco.py RCOD/configs/_base_/coco_detection.py

  • We use GLIP+APG as an example to show the training processing: shell CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./tools/dist_train.sh "--config configs/glip/glip_swin_tiny_cafr.py --work-dir /home/output 4 Training on SFR

coming soon~

Citation

If you use this toolbox or benchmark datasets in your research, please cite this project.

@article{rcod, title={Toward Realistic Camouflaged Object Detection: Benchmarks and Method}, author={Xin, Zhimeng and Wu, Tianxu and Chen, Shiming and Ye, Shuo and Xie, Zijing and Zou, Yixiong and You, Xinge and Guo, Yufei}, journal={arXiv preprint arXiv:2501.07297}, year={2025} }

Owner

  • Name: Zhimeng Xin
  • Login: zhimengXin
  • Kind: user
  • Location: USA
  • Company: Huazhong University of Science and Technology

Undergraduate Student. Student of Huazhong University of Science and Technology

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
  • Issues event: 1
  • Watch event: 9
  • Issue comment event: 1
  • Push event: 1
  • Public event: 1
  • Fork event: 1
Last Year
  • Issues event: 1
  • Watch event: 9
  • Issue comment event: 1
  • Push event: 1
  • Public event: 1
  • Fork event: 1

Dependencies

.github/workflows/deploy.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.circleci/docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
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
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