Create a Python 3.8 environement with CUDA 11.1.1 and GCC 9.3.0.
1. pip install -r requirements.txt
2. install pytorch=1.7.0 (higher version has some problems in following installation of dcnv2)
3. install dcnv2
cd src/lib/models/networks/DCNv2
sh make.sh
4. install cocoapi
cd src/lib/datasets/dataset/cocoapi/
sh install.sh
5. install nms
cd src/lib/external
make
6. create soft link for the data
vim link.sh
sh link.sh
Download and extract RadioGalaxyNET data from here. We expect the directory structure to be the following:
./data/coco/
annotations/ # annotation json files
annotations/train.json
annotations/val.json
train/ # train images
val/ # val images
Download 'keep1_train.json' from this link and place it in './data/coco/annotations/' along with 'train.json' and 'val.json' files.
Train base model using single gpu:
CUDA_VISIBLE_DEVICES=0 python ./src/main.py ctdet --exp_id fsod_res18 --arch resdcn_18 --save_all --batch_size 8 --num_epochs 200 --master_batch 18 --lr 5e-4 --gpus 0 --num_workers 16 --prefix 'keep1_'
To ease reproduction of our results we provide base model checkpoint here.
Place the model in ./exp/ctdet/fsod_res18/ directory.
Train with DMiner:
CUDA_VISIBLE_DEVICES=0 python ./src/main.py ctdet --exp_id siod_res18_plg_gcl --arch resdcn_18 --save_all --batch_size 8 --num_epochs 200 --master_batch 18 --lr 5e-4 --gpus 0 --num_workers 16 --prefix 'keep1_' --use_gcl --use_plg
To ease reproduction of our results we provide DMiner model checkpoint here.
Place the model in ./exp/ctdet/siod_res18_plg_gcl/ directory.
To evaluate on val images with a single GPU with new Score-aware Detection Evaluation Protocol, run:
Base model:
python ./src/test.py ctdet --exp_id fsod_res18_eval --arch resdcn_18 --keep_res --load_model './exp/ctdet/fsod_res18/model_last.pth' --resume
DMiner model:
python ./src/test.py ctdet --exp_id siod_res18_dminer --arch resdcn_18 --keep_res --load_model './exp/ctdet/siod_res18_plg_gcl/model_last.pth' --resume
MIT license.