🚀RetinaNet Horizontal Detector Based PyTorch

This is a horizontal detector RetinaNet implementation on remote sensing ship dataset (SSDD).
This re-implemented retinanet has the almost the same mAP(iou=.5) with the MMdetection.
RetinaNet Detector original paper link is here.

🌟Performance of the implemented RetinaNet Detector

Detection Performance on Offshore image.

Detection Performance on Inshore image.

🎯Experiment

The SSDD dataset, well-trained retinanet detector, resnet-50 pretrained model on ImageNet, loss curve, evaluation metrics results are below, you could follow my experiment.

• SSDD dataset BaiduYun extraction code=pa8j
• gt labels for eval BaiduYun extraction code=vqaw
• well-trained retinanet detector weight file BaiduYun extraction code=
• pre-trained ImageNet resnet-50 weight file BaiduYun extraction code=
• evaluation metrics(iou=.5)

Batch Size	Input Size	mAP (Mine)	mAP (MMdet)	Model Parameters
32	416 x 416	0.89	0.8891	32.2 M

• loss curve

• mAP metrics on training set and val set

• learning rate curve (using warmup lr rate)

💥Get Started

Installation

A. Install requirements:

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.

B. Install nms module:

cd utils/nms
make

Demo

you should download the trained weight file.

# run the simple inference script to get detection result.
python show.py

Train

A. Prepare dataset

you should structure your dataset files like this.

# dataset structure should be like this
datasets/
    -your_project_name/
        -train_set_name/
            -*.jpg
        -val_set_name/
            -*.jpg
        -annotations
            -instances_{train_set_name}.json
            -instances_{val_set_name}.json

# for example, coco2017
datasets/
    -coco2017/
        -train2017/
            -000000000001.jpg
            -000000000002.jpg
            -000000000003.jpg
        -val2017/
            -000000000004.jpg
            -000000000005.jpg
            -000000000006.jpg
        -annotations
            -instances_train2017.json
            -instances_val2017.json

B. Manual set project's hyper parameters

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

C. Train RetinaNet detector on a custom dataset with pretrianed resnet-50 from scratch

Evaluation