Create a Python 3.8 environement with CUDA 11.3.0. Then, install PyTorch 1.5+ and torchvision 0.6+:
conda install -c pytorch pytorch torchvision
Install pycocotools and scipy:
conda install cython scipy
pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
Install packages in requirements.txt.
pip install -r requirements.txt
Download and extract RadioGalaxyNET data from here. We expect the directory structure to be the following:
./RadioGalaxyNET/
annotations/ # annotation json files
train/ # train images
val/ # val images
test/ # test images
To train on a single node with 4 gpus run:
python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py --output_dir ./outputs_gal/
To ease reproduction of our results we provide model checkpoint here.
Place the model in ./outputs_gal/ directory.
To evaluate on test images with a single GPU run:
python -m torch.distributed.launch --nproc_per_node=1 --use_env main.py --eval --resume outputs_gal/checkpoint.pth
Apache 2.0 license.