[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike semantic segmentation, it uniquely labels and precisely delineates each object, crucial for tasks like object detection and medical imaging.
There are two types of instance segmentation tracking available in the Ultralytics package:
For any inquiries, feel free to post your questions in the [Ultralytics Issue Section](https://github.com/ultralytics/ultralytics/issues/new/choose) or the discussion section mentioned below.
### How do I perform instance segmentation using Ultralytics YOLOv8?
To perform instance segmentation using Ultralytics YOLOv8, initialize the YOLO model with a segmentation version of YOLOv8 and process video frames through it. Here's a simplified code example:
!!! Example
=== "Python"
```python
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
model = YOLO("yolov8n-seg.pt") # segmentation model
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter("instance-segmentation.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
Learn more about instance segmentation in the [Ultralytics YOLOv8 guide](#what-is-instance-segmentation).
### What is the difference between instance segmentation and object tracking in Ultralytics YOLOv8?
Instance segmentation identifies and outlines individual objects within an image, giving each object a unique label and mask. Object tracking extends this by assigning consistent labels to objects across video frames, facilitating continuous tracking of the same objects over time. Learn more about the distinctions in the [Ultralytics YOLOv8 documentation](#samples).
### Why should I use Ultralytics YOLOv8 for instance segmentation and tracking over other models like Mask R-CNN or Faster R-CNN?
Ultralytics YOLOv8 offers real-time performance, superior accuracy, and ease of use compared to other models like Mask R-CNN or Faster R-CNN. YOLOv8 provides a seamless integration with Ultralytics HUB, allowing users to manage models, datasets, and training pipelines efficiently. Discover more about the benefits of YOLOv8 in the [Ultralytics blog](https://www.ultralytics.com/blog/introducing-ultralytics-yolov8).
### How can I implement object tracking using Ultralytics YOLOv8?
To implement object tracking, use the `model.track` method and ensure that each object's ID is consistently assigned across frames. Below is a simple example:
!!! Example
=== "Python"
```python
from collections import defaultdict
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
track_history = defaultdict(lambda: [])
model = YOLO("yolov8n-seg.pt") # segmentation model
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter("instance-segmentation-object-tracking.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
while True:
ret, im0 = cap.read()
if not ret:
break
annotator = Annotator(im0, line_width=2)
results = model.track(im0, persist=True)
if results[0].boxes.id is not None and results[0].masks is not None:
Find more in the [Instance Segmentation and Tracking section](#samples).
### Are there any datasets provided by Ultralytics suitable for training YOLOv8 models for instance segmentation and tracking?
Yes, Ultralytics offers several datasets suitable for training YOLOv8 models, including segmentation and tracking datasets. Dataset examples, structures, and instructions for use can be found in the [Ultralytics Datasets documentation](https://docs.ultralytics.com/datasets/).
