SIDD.md

reproduce the SIDD dataset results

1. Data Preparation

• prepare data

  • mkdir ./datasets/SIDD

Download the train set and place it in ./datasets/SIDD/Data:

• google drive or 百度网盘,

• python scripts/data_preparation/sidd.py to crop the train image pairs to 512x512 patches and make the data into lmdb format.

  • it should be like:

    ./datasets/SIDD/Data
    ./datasets/SIDD/train
    ./datasets/SIDD/val
    ./datasets/SIDD/test
    ./datasets/SIDD/test/ValidationNoisyBlocksSrgb.mat
    ./datasets/SIDD/test/ValidationGtBlocksSrgb.mat

• python scripts/data_preparation/sidd.py

• to crop the train image pairs to 512x512 patches and make the data into lmdb format.

Download the evaluation data (in lmdb format) and place it in ./datasets/SIDD/val/:

• google drive or 百度网盘,
• it should be like ./datasets/SIDD/val/input_crops.lmdb and ./datasets/SIDD/val/gt_crops.lmdb

2. Training

• Your model:

  python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/SIDD/xxxx.yml --launcher pytorch
  

• 8 gpus by default. Set --nproc_per_node to # of gpus for distributed validation.

3. Evaluation

Save your pretrain model weight in ./experiments/pretrained_models/

Testing on SIDD dataset

• Your model:

python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/SIDD/xxx.yml --launcher pytorch

• Test by a single gpu by default. Set --nproc_per_node to # of gpus for distributed validation.