Then, it uses that knowledge to create new, artificial image-mask pairs to augment a small dataset of real examples. A segmentation model is trained using both.

Consequently, numerous researchers have focused on fine-tuning SAM for particular segmentation tasks. For example, MedSAM is a SAM-based model fine-tuned using over 1 million image-mask pairs spanning ...

Image segmentation, a fundamental problem in image processing, involves distinguishing the foreground from the background. Traditional image segmentation methods are typically divided into local and ...

This project demonstrates how to perform image segmentation using the K-Means Clustering algorithm with Python, OpenCV, and Scikit-learn. The goal is to reduce the image complexity by grouping similar ...

This project validates image segmentation models using Keras and OpenCV. The notebook demonstrates how to load a pre-trained model, normalize images, and use various metrics and visualizations for ...