Simple Image Segmentation using Deep Learning

A simple image segmentation project using deep learning can be approached by utilizing convolutional neural networks (CNNs). The goal is to classify each pixel in an image into distinct categories, such as foreground and background, or different object classes. A common starting point is the U-Net architecture, which is well-suited for biomedical image segmentation tasks.

To implement the project, you’ll first gather a dataset, such as the Oxford Pets dataset, which contains images of pets labeled with pixel-wise segmentation masks. After preprocessing the images and masks (resizing and normalization), you can split the dataset into training, validation, and test sets.

Next, you’ll define the U-Net model, consisting of an encoder to capture context and a decoder to enable precise localization. Train the model using an appropriate loss function, like the dice coefficient or cross-entropy loss, and optimize it with an algorithm such as Adam. After training, evaluate the model’s performance on the test set using metrics like Intersection over Union (IoU). Finally, visualize the results by overlaying the predicted masks on the original images, showcasing the model’s ability to accurately segment objects. This mini project can serve as a foundation for exploring more complex segmentation tasks and architectures in the future.