rsunet

This script is a detailed implementation of a Residual Sharp U-Net model designed for image segmentation tasks, particularly crack segmentation in this case. It uses Keras and TensorFlow as the main libraries and includes several custom functions and blocks to build and train the model. Here's a breakdown of the key sections:

Imports and Initial Setup:

Imports necessary libraries including TensorFlow, Keras, OpenCV, and others. Sets up environment variables to specify which GPUs to use. Data Loading:

Loads and preprocesses image data from specified directories. Converts images to grayscale, resizes them to 400x400 pixels, and normalizes pixel values. Model Building:

Defines the building blocks for the Residual Sharp U-Net model, such as convolutional layers, residual blocks, and sharpening blocks. Assembles these blocks into a full Sharp U-Net architecture with additional sharpening and Residual mechanisms in the skip connections. Custom Loss and Metrics:

Implements custom metrics like Jaccard Index (Intersection over Union), Dice Coefficient, and their corresponding loss functions. Training Setup:

Configures learning rate schedules, early stopping, and model checkpointing. Compiles the model with the Adam optimizer and Binary Cross-Entropy loss. Trains the model for 50 epochs, saving the best model based on validation loss. Results and Visualization:

Saves training history (loss and accuracy) to CSV files. The last part of the code, which was cut off, likely involves plotting these results to visualize training progress.