This repository contains an implementation of the Unet architecture for the identification of salt resources in images. The project utilizes the Salt Resources Identification Dataset from Kaggle and implements the encoder and decoder parts of the Unet architecture.

The implementation uses the Salt Resources Identification Dataset from Kaggle. The dataset consists of images and corresponding masks, where the masks indicate the presence of salt resources in the images. The dataset can be obtained from the Kaggle website (provide link to the dataset if available) or any other reliable source.

The Unet architecture is a convolutional neural network (CNN) architecture widely used for image segmentation tasks. It consists of an encoder part and a decoder part. The encoder part captures the contextual information of the input image, while the decoder part performs upsampling and recovers the spatial information to generate the segmentation mask.

The encoder part typically consists of convolutional and pooling layers that progressively reduce the spatial dimensions and increase the number of channels. This allows the network to capture high-level features and spatial context.

The decoder part uses upsampling and skip connections to recover the spatial information and refine the segmentation mask. Skip connections connect corresponding layers from the encoder to the decoder, allowing the network to utilize low-level features for accurate segmentation.

In this project, we have implemented the encoder and decoder parts of the Unet architecture. The encoder consists of convolutional and pooling layers, while the decoder consists of upsampling and convolutional layers with skip connections.

The implementation utilizes the Salt Resources Identification Dataset for training and evaluation. The dataset is divided into training and validation sets. The model is trained using the training set and evaluated on the validation set.

Clone the repository:

Download the Salt Resources Identification Dataset and place it in the appropriate directory.

Preprocess the dataset if necessary (e.g., resizing, normalizing, augmenting).

Run the training script/cell to train the Unet model

After training, the model can be used for inference on new images by running the inference script/cell

Provide details of the results achieved by the trained model, such as accuracy, IoU (Intersection over Union), or any other relevant evaluation metrics. Include any visualizations or sample predictions if available.