readme: ---
  tags:
  - image-to-image
  library_name: keras
  ---
  ## Model description
  This repo contains the model and the notebook [Low-light image enhancement using MIRNet](https://keras.io/examples/vision/mirnet/).
  
  Full credits go to [Soumik Rakshit](https://github.com/soumik12345)
  
  Reproduced by [Vu Minh Chien](https://www.linkedin.com/in/vumichien/) with a slight change on hyperparameters.
  
  With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as photography, security, medical imaging, and remote sensing. The MIRNet model for low-light image enhancement is a fully-convolutional architecture that learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details
  ## Dataset
  The [LoL Dataset](https://drive.google.com/uc?id=1DdGIJ4PZPlF2ikl8mNM9V-PdVxVLbQi6) has been created for low-light image enhancement. It provides 485 images for training and 15 for testing. Each image pair in the dataset consists of a low-light input image and its corresponding well-exposed reference image.
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-04
  - train_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: ReduceLROnPlateau
  - num_epochs: 50
  
  ### Training results
  
  - The results are shown in TensorBoard (Training metrics).
  
  
  ### View Model Demo 
  
  ![Model Demo](./demo.png)
    
  
  <details>
  
  <summary> View Model Plot </summary>
  
    ![Model Image](./model.png)
    
  </details>