Fine-Tune a Semantic Segmentation Model with a Custom Dataset

The article outlines a methodology for fine-tuning a semantic segmentation model using a custom dataset, detailing steps to adapt pre-trained models like DeepLabV3 or U-Net. It emphasizes the importance of dataset preparation, including annotation consistency and augmentation techniques, while also discussing hyperparameter tuning for optimal performance. This approach allows practitioners to leverage existing architectures to improve segmentation accuracy on domain-specific tasks, enhancing model adaptability and performance in real-world applications.