Data

Tools

Indexes

Applications

Experiments

Open Source Science

plnet-pr

https://github.com/rsip-njupt/plnet-pr

Science Score: 44.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (6.7%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: RSIP-NJUPT
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 13.5 MB
Statistics
  • Stars: 4
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed over 2 years ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

PLNet-PR

This an official Pytorch implementation of our paper "Object Detection in Remote Sensing Imagery Based on Prototype Learning Network with Proposal Relation". The specific details of the model are as follows. EGRPLNet

Datasets

  • DIOR is a large-scale benchmark dataset for object detection in optical remote sensing images, which consists of 23,463 images and 192,518 object instances covering 20 object classes annotated with horizontal bounding boxes. These 20 object classes are aircraft, airport, baseball field, basketball court, bridge, chimney, dam, highway rest area, highway toll station, port, golf course, ground track and field, footbridge, vessel, stadium, tank, tennis court, train station, vehicles, and windmill.
  • HRRSD contains 21,761 images acquired from Google Earth and Baidu Map with the spatial resolution from 0.15-m to 1.2-m. There are 55,740 object instances in HRRSD. HRRSD contains 13 categories of RSI objects. The 13 categories are: aircraft, baseball field, basketball court, bridge, intersection, athletic field, port, parking lot, ship, storage tank, T-intersection, tennis court, and car. Moreover, this dataset is divided as several subsets, image numbers in each subset are 5401 for ‘train’, 5417 for ‘val’, and 10943 for ‘test’.
  • NWPU VHR-10 is a publicly available 10-class geospatial object detection dataset used for research purposes only. These ten classes of objects are airplane, ship, storage tank, baseballdiamond, tennis court, basketball court, ground track field, harbor, bridge, and vehicle. This dataset contains totally 800 very-high-resolution (VHR) remote sensing images that were cropped from Google Earth and Vaihingen dataset and then manually annotated by experts. **** # Train on the DIOR python tools/train.py configs/dior/faster-rcnn_r50_egrpl_ms_1x_dior.py **** # Test on the DIOR python tools/test.py configs/dior/faster-rcnn_r50_egrpl_ms_1x_dior.py [your_weight_path] --tta **** # Results All the results presented here are referenced from the original paper. | Dataset | mAP (%) | | :----------: |:------:| | DIOR | 75.8 | | HRRSD | 93.3 | | NWPU VHR-10 | 95.6 | **** # Citation If you find this paper useful, please cite: **** # Contact TengFei Ma: matengfei_1013@163.com

Owner

  • Name: RSIP-NJUPT
  • Login: RSIP-NJUPT
  • Kind: organization

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
  • Watch event: 6
Last Year
  • Watch event: 6

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
requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.1.0
  • mmengine >=0.7.1,<1.0.0
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 *
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
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • parameterized * test
  • protobuf <=3.20.1 test
  • psutil * test
  • pytest * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements.txt pypi
setup.py pypi