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YOLOv8 segmentation combines object detection with pixel-level instance masks, allowing the model to identify both where an object is located and which pixels belong to it. Its main advantages include fast inference, support for multiple classes, custom dataset training, and practical deployment options. However, segmentation also requires more computation, more detailed annotations, and more memory […]

YOLOv8 with DeepSORT combines fast object detection with multi-object tracking. YOLOv8 detects objects in each video frame, while DeepSORT links those detections across frames and assigns persistent tracking IDs. DeepSORT extends SORT by adding deep appearance features, which helps reduce identity switches and improves tracking through short occlusions. Ultralytics itself currently ships trackers such as

Training YOLOv8 OBB on a custom dataset allows the model to detect objects using rotated bounding boxes instead of standard horizontal rectangles. OBB stands for Oriented Bounding Box, and it is especially useful when objects can appear at different angles, such as ships, aircraft, vehicles, buildings, text regions, and objects in aerial or satellite images.

Training YOLOv8 segmentation on a custom dataset allows you to detect objects and generate pixel-level masks that describe their exact shapes. Unlike standard object detection, which predicts rectangular bounding boxes, YOLOv8 instance segmentation predicts a separate mask for each detected object. The YOLOv8 family includes dedicated segmentation checkpoints such as yolov8n-seg.pt, yolov8s-seg.pt, yolov8m-seg.pt, yolov8l-seg.pt, and

YOLOv8 segmentation masks describe the pixel-level shape of each detected object rather than representing it only with a rectangular bounding box. A YOLOv8 segmentation result can include bounding boxes, class IDs, confidence scores, binary or probability-based mask data, and polygon coordinates for each detected instance. In the Ultralytics Python API, segmentation information is exposed through

The YOLOv8 segmentation label format represents each object with a class ID followed by a sequence of normalized polygon coordinates. Unlike normal object detection labels, segmentation labels do not describe an object only with a rectangular bounding box. Instead, multiple (x, y) points trace the object’s shape, allowing YOLOv8 segmentation models to learn pixel-level object

YOLOv8 Pose keypoints represent important landmark positions on detected objects, such as the nose, shoulders, elbows, wrists, hips, knees, and ankles in human pose estimation. A YOLOv8 Pose prediction can include bounding boxes, class information, detection confidence, keypoint coordinates, and keypoint confidence values. Understanding this output format is important when building pose tracking, sports analysis,

YOLOv8 hand pose estimation uses pose/keypoint detection to locate hands and predict landmark positions representing the wrist, fingers, and individual finger joints. YOLOv8 supports pose training and inference, so it can be fine-tuned on a hand-keypoint dataset for applications such as gesture recognition, sign-language systems, human-computer interaction, robotics, and AR/VR. A common hand landmark structure

The YOLOv8 Pose dataset format extends the standard YOLO object detection format by adding keypoint coordinates to each labeled object. Every training image has a corresponding text label file containing the class ID, normalized bounding box coordinates, and the defined keypoints. Depending on the dataset configuration, each keypoint can contain either x, y coordinates or

The YOLOv8 COCO class list contains 80 object categories that pretrained YOLOv8 detection models can recognize. These classes come from the COCO dataset and include common objects such as people, cars, animals, furniture, food items, sports equipment, and household objects. Each class has a unique ID from 0 to 79, which YOLOv8 uses internally during