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Medical_Image

Results

  • Results on patches
  • Results on patients
Performance Autoencoder approach:

results1

Performance CNN approach:

results1

1. Create Environment

  • Make Conda Environment
Ficar com crear el environment desde el yml
conda create -n x python=3.7
conda activate x

 

2. Prepare Dataset

Download the following datasets:

LOL-v1 Baidu Disk (code: cyh2), Google Drive

 

3. Testing

Download our models from Baidu Disk (code: cyh2) or Google Drive. Put them in folder pretrained_weights

# Autoencoder
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_LOL_v1.yml --weights pretrained_weights/LOL_v1.pth --dataset LOL_v1
# CNN
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_LOL_v2_real.yml --weights pretrained_weights/LOL_v2_real.pth --dataset LOL_v2_real
  • Evaluating the Params and FLOPS of models

 

4. Training

Feel free to check our training logs from Baidu Disk (code: cyh2) or Google Drive

# Autoencoder
python3 basicsr/train.py --opt Options/RetinexFormer_LOL_v1.yml
# CNN
python3 basicsr/train.py --opt Options/RetinexFormer_LOL_v2_real.yml