Image segmentation plays a key role in computer vision applications such as object recognition and autonomous driving, and the Segment Anything Model 2 (SAM2) enhances this process by allowing users to segment objects in images with minimal input through a web browser. The SAM2 model employs an encoder-decoder architecture, where the encoder processes images to generate high-dimensional embeddings, and the decoder uses these embeddings and user-provided points to create segmentation masks. Running SAM2 in the browser offers benefits like enhanced privacy, as images are processed locally, and greater accessibility, negating the need for specialized software. The implementation involves using ONNX Runtime Web to load and run the model, supporting real-time feedback and interactivity. The browser-based application includes a user interface where users can upload images, add interaction points, and observe segmentation masks update in real-time, enabling quick iterations. This approach not only ensures user data privacy but also democratizes access to advanced machine learning models by allowing them to run efficiently in the browser, paving the way for more sophisticated models to be used in similar settings as web technologies advance.