Fix mkdocs.yml raw image URLs (#14213)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

docs/en/guides/instance-segmentation-and-tracking.md

@@ -135,3 +135,114 @@ There are two types of instance segmentation tracking available in the Ultralyti
 ## Note
 
 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.
+
+## FAQ
+
+### 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))
+
+        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 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:
+                masks = results[0].masks.xy
+                track_ids = results[0].boxes.id.int().cpu().tolist()
+
+                for mask, track_id in zip(masks, track_ids):
+                    annotator.seg_bbox(mask=mask, mask_color=colors(track_id, True), track_label=str(track_id))
+
+            out.write(im0)
+            cv2.imshow("instance-segmentation-object-tracking", im0)
+            if cv2.waitKey(1) & 0xFF == ord("q"):
+                break
+
+        out.release()
+        cap.release()
+        cv2.destroyAllWindows()
+        ```
+
+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/).