Instance Segmentation and Tracking using Ultralytics YOLO11 🚀

Instance Segmentation and Tracking using Ultralytics YOLO11 🚀#

What is Instance Segmentation?#

Ultralytics YOLO11 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:

Instance Segmentation with Class Objects: Each class object is assigned a unique color for clear visual separation.
Instance Segmentation with Object Tracks: Every track is represented by a distinct color, facilitating easy identification and tracking.

Watch: Instance Segmentation with Object Tracking using Ultralytics YOLO11

Samples#

Instance Segmentation	Instance Segmentation + Object Tracking

Ultralytics Instance Segmentation 😍	Ultralytics Instance Segmentation with Object Tracking 🔥

!!! example “Instance Segmentation and Tracking”

=== "Instance Segmentation"

    ```python
    import cv2

    from ultralytics import YOLO
    from ultralytics.utils.plotting import Annotator, colors

    model = YOLO("yolo11n-seg.pt")  # segmentation model
    names = model.model.names
    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))

    while True:
        ret, im0 = cap.read()
        if not ret:
            print("Video frame is empty or video processing has been successfully completed.")
            break

        results = model.predict(im0)
        annotator = Annotator(im0, line_width=2)

        if results[0].masks is not None:
            clss = results[0].boxes.cls.cpu().tolist()
            masks = results[0].masks.xy
            for mask, cls in zip(masks, clss):
                color = colors(int(cls), True)
                txt_color = annotator.get_txt_color(color)
                annotator.seg_bbox(mask=mask, mask_color=color, label=names[int(cls)], txt_color=txt_color)

        out.write(im0)
        cv2.imshow("instance-segmentation", im0)

        if cv2.waitKey(1) & 0xFF == ord("q"):
            break

    out.release()
    cap.release()
    cv2.destroyAllWindows()
    ```

=== "Instance Segmentation with Object Tracking"

    ```python
    from collections import defaultdict

    import cv2

    from ultralytics import YOLO
    from ultralytics.utils.plotting import Annotator, colors

    track_history = defaultdict(lambda: [])

    model = YOLO("yolo11n-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:
            print("Video frame is empty or video processing has been successfully completed.")
            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:
            masks = results[0].masks.xy
            track_ids = results[0].boxes.id.int().cpu().tolist()

            for mask, track_id in zip(masks, track_ids):
                color = colors(int(track_id), True)
                txt_color = annotator.get_txt_color(color)
                annotator.seg_bbox(mask=mask, mask_color=color, label=str(track_id), txt_color=txt_color)

        out.write(im0)
        cv2.imshow("instance-segmentation-object-tracking", im0)

        if cv2.waitKey(1) & 0xFF == ord("q"):
            break

    out.release()
    cap.release()
    cv2.destroyAllWindows()
    ```

`seg_bbox` Arguments#

Name	Type	Default	Description
`mask`	`array`	`None`	Segmentation mask coordinates
`mask_color`	`RGB`	`(255, 0, 255)`	Mask color for every segmented box
`label`	`str`	`None`	Label for segmented object
`txt_color`	`RGB`	`None`	Label color for segmented and tracked object

Note#

For any inquiries, feel free to post your questions in the Ultralytics Issue Section or the discussion section mentioned below.

FAQ#

How do I perform instance segmentation using Ultralytics YOLO11?#

To perform instance segmentation using Ultralytics YOLO11, initialize the YOLO model with a segmentation version of YOLO11 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("yolo11n-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))

    while True:
        ret, im0 = cap.read()
        if not ret:
            break

        results = model.predict(im0)
        annotator = Annotator(im0, line_width=2)

        if results[0].masks is not None:
            clss = results[0].boxes.cls.cpu().tolist()
            masks = results[0].masks.xy
            for mask, cls in zip(masks, clss):
                annotator.seg_bbox(mask=mask, mask_color=colors(int(cls), True), det_label=model.model.names[int(cls)])

        out.write(im0)
        cv2.imshow("instance-segmentation", im0)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break

    out.release()
    cap.release()
    cv2.destroyAllWindows()
    ```

Learn more about instance segmentation in the Ultralytics YOLO11 guide.

What is the difference between instance segmentation and object tracking in Ultralytics YOLO11?#

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 YOLO11 documentation.

Why should I use Ultralytics YOLO11 for instance segmentation and tracking over other models like Mask R-CNN or Faster R-CNN?#

Ultralytics YOLO11 offers real-time performance, superior accuracy, and ease of use compared to other models like Mask R-CNN or Faster R-CNN. YOLO11 provides a seamless integration with Ultralytics HUB, allowing users to manage models, datasets, and training pipelines efficiently. Discover more about the benefits of YOLO11 in the Ultralytics blog.

How can I implement object tracking using Ultralytics YOLO11?#

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: