RetinaNet-PyTorch - A RetinaNet Pytorch Implementation on remote sensing images and has the similar mAP result with RetinaNet in MMdetection

conda create -n retinanet python=3.7
conda activate retinanet
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: If you meet some troubles about installing environment, you can see the check.txt for more details.

cd utils/HBB_NMS_GPU
make

# config.py
# 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  
  
inshore_data_path = r'/$ROOT_PATH/SSDD_data_InShore/'  # absolute Inshore data path  
offshore_data_path = r'/$ROOT_PATH/SSDD_data_OffShore/'  # absolute Offshore data path  

# An example  
$ROOT_PATH
    -SSDD_data/
        -train/  # train set 
	    -*.jpg
	-val/  # val set
	    -*.jpg
	-annotations/  # gt label in json format (for coco evaluation method)  
	    -instances_train.json  
	    -instances_val.json  
	-ground-truth/  
	    -*.txt  # gt label in txt format (for voc evaluation method and evaluae inshore and offshore scence)  
	-train-ground-truth/
	    -*.txt  # gt label in txt format (for voc evaluation method)
    -SSDD_data_InShore/  
        -images/
	    -*.jpg  # inshore scence images
	-ground-truth/
	    -*.txt  # inshore scence gt labels  
    -SSDD_data_OffShore/  
        -images/  
	    -*.jpg  # offshore scence images
	-ground-truth/  
	    -*.txt  # offshore scence gt labels

    -Output/
        -checkpoints/
	    - the path of saving tensorboard log event
	-evaluate/  
	    - the path of saving model detection results for evaluate (coco/voc/inshore/offshore)  

# download and put the well-trained pth file in checkpoints/ folder 
# and run the simple inferene script to get detection result  
# you can find the model output predict.jpg in show_result/ folder.  

python show.py --chkpt 54_1595.pth --result_path show_result --pic_name demo1.jpg  

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.  

# with batchsize 32 and using voc evaluation method during training for 50 epochs  
python train.py --batch_size 32 --epoch 50 --eval_method voc  
  
# with batchsize 32 and using coco evalutation method during training for 50 epochs  
python train.py --batch_size 32 --epoch 50 --eval_method coco  

Note: If you find classification loss change slowly, please be patient, it's not a mistake.

python eval.py --device 0 --evaluate True --FPS False --Offshore False --Inshore False --chkpt 54_1595.pth

python eval.py --device 0 --evaluate False --FPS False --Offshore True --Inshore True --chkpt 54_1595.pth

python eval.py --device 0 --evaluate False --FPS True --Offshore False --Inshore Fasle --chkpt 54_1595.pth