This is an implementation of RetinaNet in Pytorch, using ResNet as backbone and FPN.
1.Download PASCAL VOC 2012 trainval datasets and unzip it. Its path should be '{root_dir}/VOCdevkit/..'
2.Download this repo
git clone git@github.com:qqadssp/RetinaNet.git
cd RetinaNet
3.Download pretrained weights from https://download.pytorch.org/models/resnet50-19c8e357.pth
cd checkpoint
wget https://download.pythorch.org/models/resnet50-19c8e357.pth
cd ..
4.Initialize the model
python init.py
5.Modify configs file in 'config'. For VOC datatsets, modify 'TRAIN: DATASETS_DIR' with your {root_dir}
6.Train the model
python train.py --cfg ./configs/RetinaNet_ResNet50_FPN_VOC.yaml
1.Download PASCAL VOC 2012 test datasets and unzip it. Its path should be '{root_dir}/VOCdevkit_test/..'
2.Modify config file in 'configs'. For VOC datasets, modify 'TEST: DATASETS_DIR' with your {root_dir}, and 'TEST: WEIGHTS' with the trained weights in 'checkpoint'
3.Test the model. The result files will be in 'result'.
python test.py --cfg ./configs/RetinaNet_ResNet50_FPN_VOC.yaml
