Create README.md

README.md

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+---
+tags:
+- image-segmentation
+library_name: keras
+---
+## Model description
+Full credits go to: [Vu Minh Chien](https://www.linkedin.com/in/vumichien/)
+
+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
+## 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
+**Model architecture**:
+- UNet with a pretrained DenseNet 201 backbone.
+
+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.
+
+
+### View Model Demo 
+
+![Model Demo](./demo.png)
+  
+
+<details>
+
+<summary> View Model Plot </summary>
+
+  ![Model Image](./model.png)
+  
+</details>