Segmentation
Explore image segmentation in computer vision using Hugging Face pipelines. Understand different segmentation types—semantic, instance, and panoptic—and see how models like SegFormer enable pixel-wise labeling for detailed scene interpretation. Learn to visualize segmentation output and grasp applications in fields such as autonomous driving and medical imaging.
Segmentation goes beyond object detection by assigning a label to every pixel in an image.
Instead of simply marking objects with bounding boxes, segmentation produces masks that outline the exact shapes of objects. This pixel-level understanding enables machines to interpret complex scenes with much greater detail.
Applications include autonomous driving on roads, medical scans that identify tumors, satellite images that map terrain, and robotic systems that navigate cluttered environments.
Types of segmentation
Segmentation can be categorized based on how precisely the model separates objects and how it treats multiple instances within the scene.
Semantic segmentation: Classifies each pixel into a category, such as road, car, or tree. It does not distinguish between individual instances of the same object, so all cars in an image share the same label. This type is useful when you care about understanding the overall scene composition rather than individual objects.
Instance segmentation: It's a step further. It not only classifies pixels but also distinguishes between different instances of the same object class. For example, each person or car in a scene receives a separate label, which is crucial for applications like crowd monitoring, retail analytics, or robot manipulation.
Panoptic segmentation: Combines semantic and instance segmentation. Every pixel is assigned a semantic label and an instance ID if applicable. This holistic approach provides a complete understanding of the scene, making it particularly useful for autonomous driving or complex scene analysis. ...