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--- |
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tags: |
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- image-segmentation |
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library_name: keras |
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--- |
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## Model description |
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Full credits go to: [Vu Minh Chien](https://www.linkedin.com/in/vumichien/) |
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With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in photography, security, medical imaging, and remote sensing. The MIRNet model for low-light image enhancement, 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 |
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## Dataset |
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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. |
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## Training procedure |
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### Training hyperparameters |
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**Model architecture**: |
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- UNet with a pretrained DenseNet 201 backbone. |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-04 |
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- train_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: ReduceLROnPlateau |
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- num_epochs: 50 |
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### Training results |
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- The results are shown in TensorBoard. |
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### View Model Demo |
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<details> |
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<summary> View Model Plot </summary> |
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</details> |
