This repository contains the implementation of image segmentation using U-Nets and YOLO v9. Image segmentation is the process of partitioning an image into multiple segments (sets of pixels) to simplify or change the representation of an image into something more meaningful and easier to analyze.
Image segmentation is a computer vision task that involves dividing an image into multiple segments or regions, typically to identify and isolate objects or regions of interest. Each segment corresponds to a different object or part of an object. This is particularly useful in applications such as medical imaging, autonomous driving, and object detection, where precise identification of regions within an image is crucial.
U-Net is a type of convolutional neural network architecture that was originally designed for biomedical image segmentation. It is named for its U-shaped structure, which consists of a contracting path (encoder) and an expansive path (decoder). The contracting path captures the context and high-level features of the image, while the expansive path enables precise localization and segmentation by combining the context with high-resolution features.
A# pplications of U-Nets:
- Medical Imaging: Segmenting tumors, organs, and other structures in medical scans (e.g., MRI, CT scans).
- Autonomous Driving: Segmenting roads, pedestrians, and other objects in driving scenes.
- Satellite Imaging: Segmenting different land cover types in satellite images.
- Agriculture: Segmenting crops and other vegetation in agricultural images.
- Python 3.8+
- TensorFlow/Keras
- PyTorch
- YOLO v9