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:rocket:RetinaNet Oriented Detector Based PyTorch

This is an oriented detector Rotation-RetinaNet implementation on Optical and SAR ship dataset.

• SAR ship dataset (SSDD): SSDD Dataset link
• Optical ship dataset (HRSC): HRSC Dataset link
• RetinaNet Detector original paper link is here.

:star2:Performance of the implemented Rotation-RetinaNet Detector

Detection Performance on HRSC Dataset.

Detection Performance on SSDD Dataset.

:dart:Experiment

:boom:Get Started

Installation

A. Install requirements:

conda create -n rotate python=3.7
conda activate rotate
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=11.0 -c pytorch
pip install -r requirements.txt

Note: the opencv version must > 4.5.1

B. Install rotation_nms and rotation_overlaps module:

Only need one Step:
make

Demo

A. Set project's data path

you should set project's data path in yml file first.

# .yml file
# Note: all the path should be absolute path.
data_path = r'/$ROOT_PATH/SSDD_data/'  # absolute data root path
output_path = r'/$ROOT_PATH/Output/'  # absolute model output path

# For example
$ROOT_PATH
    -HRSC/
        -train/  # train set
	   -Annotations/
	      -*.xml
	   -images/
	      -*.jpg
	-test/  # test set
	   -Annotations/
	      -*.xml
           -images/
	      -*.jpg
	-ground-truth/
	   -*.txt  # gt label in txt format (for voc evaluation method)

    -SSDD/
       -train/  # train set
	  -Annotations/
	     -*.xml
	  -images/
	     -*.jpg
       -test/  # test set
	  -Annotations/
	     -*.xml
          -images/
	     -*.jpg
       -ground-truth/
	  -*.txt  # gt label in txt format (for voc evaluation method)
	  

    -Output/
        -checkpoints/
	    - the path of saving chkpt files
	-tensorboard/
	   - the path of saving tensorboard event files
	-evaluate/
	    - the path of saving model detection results for evaluate (voc method method)
	-log.log (save the loss and eval result)
	-yml file (config file)

B. Run the show.py

# for SSDD dataset
python show.py --config_file ./configs/retinanet_r50_fpn_ssdd.yml --chkpt {chkpt.file} --result_path show_result/RSSDD --pic_name demo1.jpg

# for HRSC dataset
python show.py --config_file ./configs/retinanet_r50_fpn_hrsc.yml --chkpt {chkpt.file} --result_path show_result/HRSC --pic_name demo1.jpg

Train

A. Prepare dataset

you should structure your dataset files as shown above.

B. Manual set project's hyper parameters

you should manual set projcet's hyper parameters in config file.

1. data file structure (Must Be Set !)
   has shown above.

2. Other settings (Optional)
   if you want to follow my experiment, dont't change anything.

C. Train Rotation-RetinaNet on SSDD or HRSC dataset with resnet-50 from scratch

C.1 Download the pre-trained resnet-50 pth file

you should download the pre-trained resnet-50 pth first and put the pth file in resnet_pretrained_pth/ folder.

C.2 Train Rotation-RetinaNet Detector on SSDD or HRSC Dataset with pre-trained pth file

# train model on SSDD dataset from scratch
python train.py --config_file ./configs/retinanet_r50_fpn_ssdd.yml --resume None

# train model on HRSC dataset from scratch
python train.py --config_file ./configs/retinanet_r50_hrsc.yml --resume None

D. Resume training Rotation-RetinaNet detector on SSDD or HRSC dataset

# train model on SSDD dataset from specific epoch
python train.py --config_file ./configs/retinanet_r50_fpn_ssdd.yml --resume {epoch}_{step}.pth

# train model on HRSC dataset from specific epoch
python train.py --config_file ./configs/retinanet_r50_hrsc.yml --resume {epoch}_{step}.pth

Evaluation

A. evaluate model performance on SSDD or HRSC val set.

python eval.py --Dataset SSDD --config_file ./configs/retinanet_r50_fpn_ssdd.yml --evaluate True --chkpt {epoch}_{step}.pth
python eval.py --Dataset HRSC --config_file ./configs/retinanet_r50_fpn_hrsc.yml --evaluate True --chkpt {epoch}_{step}.pth

:bulb:Inferences

Thanks for these great work.
https://github.com/open-mmlab/mmrotate
https://github.com/ming71/Rotated-RetinaNet

:fast_forward:Zhihu Link

zhihu article