We (IIL2) propose Recurrent MobileNet for light parameters raw image denoising. Our code is based on Pytorch, we also convert model weights to MegEngine for inference.
conda env create -f requirements.yaml
conda activate recurrent_mobilenet
Number of parameters of the model is restrict to 100k. To check the number of
parameters, you could run each file in models directory.
cd models
python recurrent_mobilenet.py
cd test_models
python test.py --path path_to_dataset
it should generate a result.bin to current directory. The path_to_dataset is organized
the same as downloaded:
path_to_dataset
|--burst_raw
|--competition_train_input.0.2.bin
|--competition_train_gt.0.2.bin
|--competition_test_input.0.2.bin
We also divide the last 1024 pairs in the training set as our validation set, you can also validate on our dataset.
cd validate_models
python validate.py --path path_to_dataset
cd train_models
python distributed_train.py --path path_to_dataset --worldsize 8