How I Trained U-Net to Detect Dolphins - Segmentation Tutorial

Learn to build a complete U-Net image segmentation model from scratch using Python, TensorFlow/Keras, and OpenCV in this comprehensive hands-on tutorial. Follow the entire deep learning pipeline using the Northumberland Dolphin Dataset, starting from raw images and JSON annotations to create a trained model that predicts dolphin segmentation masks in real-time. Master the process of automatically generating binary masks from JSON annotations, preprocessing and resizing image/mask data for model training, and building a U-Net segmentation architecture in TensorFlow/Keras. Discover how to train your model effectively using callbacks like EarlyStopping and ModelCheckpoint, visualize training accuracy and loss with Matplotlib, and run live inference on test images while displaying results with OpenCV. The tutorial covers dataset preparation, installation requirements, coding implementation, model architecture construction, training procedures, and testing phases, making it perfect for anyone working with custom datasets and seeking practical experience in computer vision and deep learning segmentation tasks.