361642296 / shipsardetect_mmdetection

This is an unofficial implementation for "Ship Detection in SAR Images Based on Feature Enhancement Swin Transformer and Adjacent Feature Fusion" article

Introduction

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.5+.

Major features

• Modular Design

  We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.

• Support of multiple frameworks out of box

  The toolbox directly supports popular and contemporary detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc.

• High efficiency

  All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including Detectron2, maskrcnn-benchmark and SimpleDet.

• State of the art

  The toolbox stems from the codebase developed by the MMDet team, who won COCO Detection Challenge in 2018, and we keep pushing it forward.

Apart from MMDetection, we also released a library mmcv for computer vision research, which is heavily depended on by this toolbox.

Installation

Please refer to Installation for installation instructions.

Getting Started

Please see get_started.md for the basic usage of MMDetection. We provide colab tutorial and instance segmentation colab tutorial, and other tutorials for:

• with existing dataset
• with new dataset
• with existing dataset_new_model
• learn about configs
• customize_datasets
• customize data pipelines
• customize_models
• customize runtime settings
• customize_losses
• finetuning models
• export a model to ONNX
• export ONNX to TRT
• weight initialization
• how to xxx

FAQ

Please refer to FAQ for frequently asked questions.

Citation

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

@article{mmdetection,
  title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
  author  = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
             Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
             Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
             Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
             Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
             and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
  journal= {arXiv preprint arXiv:1906.07155},
  year={2019}
}

License

This project is released under the Apache 2.0 license.

Command line

Activate conda environment : conda activate ShipSARDetect_mmdetection

• Run for Faster R-CNN ResNet : python tools\train.py configs\ssdd\faster_rcnn_r50_fpn_ssdd.py
• Run for Faster R-CNN VGG16 : python tools\train.py configs\ssdd\faster_rcnn_vgg16_fpn_ssdd.py
• Run for Cascade R-CNN : python tools\train.py configs\ssdd\cascade_rcnn_r50_fpn_ssdd.py
• Run for Cascade R-CNN Swin : python tools\train.py configs\ssdd\cascade_rcnn_swin_fpn_ssdd.py

python tools\test.py configs\faster_rcnn\faster_rcnn_r50_fpn_ssdd.py work_dirs\faster_rcnn_r50_fpn_ssdd\latest.pth --show-dir results