YOLOv8 Segmentation
Instance segmentation goes beyond object detection by not only identifying individual objects in an image but also accurately segmenting them from the surrounding background. The result of an instance segmentation model is a collection of masks or contours outlining each object in the image, accompanied by class labels and confidence scores for each object. Instance segmentation is particularly valuable when detailed information about object boundaries and shapes is necessary, in addition to their spatial locations within the image. For this task, we used YOLOv8 which enables instance segmentation with mAP mask of 43.4 where the mAP mask metric measures the average precision of how well the model’s predicted masks match the ground truth masks for each object instance in the image. An example of the output of the segmentation model is shown below:
Example
from dronevis.models import YOLOv8Segmentation model = YOLOv8Segmentation() model.load_model() model.detect_webcam()
YOLOv8 Segmentation Class
- class dronevis.models.YOLOv8Segmentation(track=False, show_conf=True, show_labels=True)
YOLOv8 model implementation for object segmentation