dmm

https://github.com/another-0/dmm

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
Links to: arxiv.org
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (9.6%) to scientific vocabulary

Last synced: 10 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: Another-0
License: apache-2.0
Language: Python
Default Branch: main
Size: 9.71 MB

Statistics

Stars: 16
Watchers: 2
Forks: 0
Open Issues: 6
Releases: 0

Created almost 2 years ago · Last pushed over 1 year ago

Metadata Files

Readme License Citation

《DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing》

The official implementation of the paper.

Dataset

Download the dataset from the repository https://github.com/VisDrone/DroneVehicle, then run the following code to crop the white borders: shell python tools/data_process.py
Run the following code to process the labels (since the original labels for the "freight-car" category are inconsistent and contain errors such as "*", we have unified them to "freight-car" in the code): shell python tools/VOC2DOTA.py

pretrained weights: BaiduYun [code: jwqx]

Envirenment

CUDA==11.8

Pytorch==2.1.2

mmcv==2.1.0

mmdet==3.3.0

mmengine==0.10.5

numpy==1.26.4

You can follow the steps below to create an virtual environment:

install all dependencies: ``` conda create -n dmm python=3.10 conda activate dmm

conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=11.8 -c pytorch -c nvidia

pip install -U openmim mim install mmdet pip install numpy==1.26.4 ```

You might encounter the following, Downgrade the pip version to 24.0 (pip install pip==24.0) Ignoring mmcv: markers 'extra == "mim"' don't match your environment Ignoring mmengine: markers 'extra == "mim"' don't match your environment

Follow the https://github.com/MzeroMiko/VMamba Getting Started Step 2, install selective_scan==0.0.2
Clone the code and install: git clone https://github.com/Another-0/DMM cd DMM pip install -v -e .

Run

train python ./tools/train.py ${CONFIG_FILE}
test python ./tools/test.py ${CONFIG_FILE} ${CHECKPOINT}

For more command-line arguments, please refer to the code details.

Acknowledgment

Our codes are mainly based on MMRotate and VMamba. Many thanks to the authors!

Citation

Please cite our work if you find our work and codes helpful for your research. @article{zhou2024dmm, title={DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing}, author={Zhou, Minghang and Li, Tianyu and Qiao, Chaofan and Xie, Dongyu and Wang, Guoqing and Ruan, Ningjuan and Mei, Lin and Yang, Yang}, journal={arXiv preprint arXiv:2407.08132}, year={2024} }

Owner

Name: 玹
Login: Another-0
Kind: user

Repositories: 1
Profile: https://github.com/Another-0

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMRotate Contributors"
title: "OpenMMLab rotated object detection toolbox and benchmark"
date-released: 2022-02-18
url: "https://github.com/open-mmlab/mmrotate"
license: Apache-2.0

GitHub Events

Total

Issues event: 4
Watch event: 11
Issue comment event: 1
Push event: 2
Fork event: 1

Last Year

Issues event: 4
Watch event: 11
Issue comment event: 1
Push event: 2
Fork event: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time

Total issues: 2
Total pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Total issue authors: 2
Total pull request authors: 0
Average comments per issue: 0.0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 2
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 2
Pull request authors: 0
Average comments per issue: 0.0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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