Boost Your Dataset with YOLOv8 Auto-Label Segmentation

Last Updated on 11/11/2025 by Eran Feit

Boost Your Dataset with yolov8 auto-label segmentation and stop wasting time on manual annotations.
In this tutorial, we’ll use a pre-trained YOLOv8 segmentation model to automatically detect objects in each video frame, draw high-quality masks, and save labeled outputs you can directly reuse for training or fine-tuning.
You’ll see how to process video streams frame by frame, organize segmented instances into class-based folders, and instantly turn raw footage into a structured dataset.
Whether you’re working on real-world detection, segmentation, or rapid prototyping, this workflow gives you a clean, scalable way to generate powerful datasets with minimal effort.

YOLOv8 Video Segmentation for Real Projects

This tutorial shows how to turn a regular video into a rich, segmented stream using a pre-trained YOLOv8 segmentation model.
You’ll see how to load yolov8n-seg, run segmentation on each frame, overlay masks in real time, save an output video, and export labeled instance frames to disk.
The entire walkthrough directly delivers what the title promises: a practical, fast, and copy-paste-ready YOLOv8 video segmentation tutorial you can plug into your own projects.
Whether you work with surveillance, sports, vehicles, or any real-world footage, this exact pattern helps you move from raw pixels to structured, segment-aware outputs.

You can download the code here : https://ko-fi.com/s/16c72078f3

Link for the post in Medium users : https://medium.com/@feitgemel/boost-your-dataset-with-yolov8-auto-label-segmentation-eb782002e0f4

 Want to get started with Computer Vision or take your skills to the next level ?

If you’re just beginning, I recommend this step-by-step course designed to introduce you to the foundations of Computer Vision – Complete Computer Vision Bootcamp With PyTorch & TensorFlow

If you’re already experienced and looking for more advanced techniques, check out this deep-dive course – Modern Computer Vision GPT, PyTorch, Keras, OpenCV4

Part 1 — Getting YOLOv8 Auto-Label Segmentation Ready for Video Segmentation

Here we set up the core ingredients: OpenCV for video handling, Ultralytics YOLOv8 for segmentation, and a clean path structure to read your input video and write processed results.
We use the yolov8n-seg.pt model, a lightweight segmentation variant that balances speed and accuracy, making it ideal for real-time or near real-time video workloads on common GPUs — and often even CPU.
We also prepare a VideoCapture to read frames and a VideoWriter to export the annotated segmentation video alongside your saved instance frames.
This foundation turns your script into a reusable yolov8 video segmentation tutorial pattern you can adapt to any folder, filename, or deployment scenario.

You can find the video file (along with the code) here : https://ko-fi.com/s/16c72078f3

Here is a sample image from the video file :

Detailed Walkthrough & Code (Part 1)

### Import OpenCV for handling video streams, image operations, and display windows. import cv2   ### Import the YOLO class from Ultralytics to load and run the YOLOv8 segmentation model. from ultralytics import YOLO  ### Import the Annotator helper and a color utility to draw segmentation masks and labels nicely on each frame. from ultralytics.utils.plotting import Annotator , colors  ### Import os to manage folders and file paths when saving segmented instances. import os    ### Load the pre-trained YOLOv8 nano segmentation model for fast, real-time-friendly inference on video frames. model = YOLO('yolov8n-seg.pt')  ### Extract the class names from the underlying model so we can map class IDs to human-readable labels. names = model.model.names  ### Print the list of class names to verify which categories the segmentation model can detect. print(names)  ### Open the input video file that we want to process with YOLOv8 segmentation. cap = cv2.VideoCapture("Best-Semantic-Segmentation-models/Yolo-V8/Autolabel-Segmentation/test.mp4")  ### Create a VideoWriter to save an annotated output video with segmentation overlays in MJPG format at 30 FPS. out = cv2.VideoWriter("Best-Semantic-Segmentation-models/Yolo-V8/Autolabel-Segmentation/out.avi",                       cv2.VideoWriter_fourcc(*'MJPG'), 30 ,                       (int(cap.get(3)), int(cap.get(4))))  ### Initialize a frame counter so we can generate unique filenames for saved segmented frames. numerator = 0 

Short summary for Part 1:
You loaded a YOLOv8 segmentation model, inspected its classes, opened your source video, and prepared an output writer plus a frame counter.
This sets the stage for a smooth yolov8 video segmentation tutorial pipeline that feels predictable and production-minded.
this is the starting point for YOLOv8 Auto-Label Segmentation

Part 2 — Running YOLOv8 Segmentation on Each Frame

In this part, we loop through the video frame by frame and apply YOLOv8 segmentation in real time.
Each frame goes through model.predict, giving you masks, class IDs, and boxes you can use to overlay colored segmentation regions.
When masks are available, we create an Annotator to draw segmented regions and labels; at the same time, we organize per-class folders and save labeled frames for further analysis or dataset bootstrapping.
This is where the tutorial becomes a practical yolov8 video segmentation workflow: you see objects, masks, and per-class exports — all from a compact loop.

Detailed Walkthrough & Code (Part 2)

### Start an infinite loop to read and process frames from the input video one by one. while True:     ### Read the next frame from the video stream; ret indicates success, img is the frame.     ret , img = cap.read()       ### Increment the frame counter to keep track of the current frame index for naming outputs.     numerator = numerator + 1      ### If reading failed (end of video or error), log a message and exit the loop cleanly.     if not ret:         print("Break the loop")         break       ### Run YOLOv8 segmentation prediction on the current frame to detect objects and generate masks.     results = model.predict(img)     #print(results[0].masks) # just to see mask detection      ### Only continue if YOLOv8 actually returned masks for this frame (i.e., at least one segmented object detected).     if results[0].masks is not None :         ### Extract the class IDs for all detected objects and move them to CPU as a Python list.         clss = results[0].boxes.cls.cpu().tolist()          ### Get the polygon coordinates (xy format) for all segmentation masks in the current frame.         masks = results[0].masks.xy           ### Initialize an Annotator object on the current frame so we can draw masks, labels, and boundaries.         annotator = Annotator(img , line_width=2)          ### Iterate over each detected mask and its corresponding class ID together.         for idx,  (mask , cls) in enumerate(zip(masks,clss)):             ### Convert the numeric class ID into a readable label using the model's class names.             det_label = names[int(cls)]              ### Draw the segmentation mask and a labeled bounding region for this detection using a consistent color.             annotator.seg_bbox(mask = mask,                                mask_color = colors(int(cls), True),                                det_label=det_label)              ### Build a folder path for this object class to organize exported segmented frames per category.             instance_folder = os.path.join("Best-Semantic-Segmentation-models/Yolo-V8/Autolabel-Segmentation", det_label)              ### Create the class-specific folder if it does not already exist.             os.makedirs(instance_folder, exist_ok=True)              ### Build a unique filename for this frame's annotated image using the class label and frame index.             instance_path = os.path.join(instance_folder, f"{det_label}_{str(numerator)}.png")              ### Save the current annotated frame into the class-specific folder for later review or dataset creation.             cv2.imwrite(instance_path, img) 

Notes (kept practical & honest):

• As written, each detection saves the full annotated frame under that class folder (not a cropped-only instance).
  This is still useful for quick dataset bootstrapping and review, but you can later upgrade it to apply each mask to extract true per-instance crops.
• This loop is the heart of the yolov8 instance segmentation video pipeline: detect, segment, visualize, store.
• YOLOv8 Auto-Label Segmentation – the main core for segmentation

Short summary for Part 2:
You’re now segmenting each frame with YOLOv8, drawing colorful masks, and exporting labeled frames into organized folders.
This turns raw video into a structured source of segmented visual data with almost no manual work.

Part 3 — Displaying, Saving, and Cleaning Up Gracefully

The last part keeps things user-friendly and production-safe.
You display the live segmented video so you can visually confirm results while the script runs.
At the same time, you write each processed frame into an output video file, giving you a ready-to-share demo of your YOLOv8 video segmentation tutorial results.
Finally, you release all OpenCV resources and close windows to avoid memory leaks or locked files.

Detailed Walkthrough & Code (Part 3)

    ### Show the current annotated frame in a live window so you can monitor segmentation results in real time.     cv2.imshow("img", img)      ### Write the annotated frame into the output video file for later playback or sharing.     out.write(img)      ### Listen for a key press; if the user presses 'q', stop processing early and break the loop.     if cv2.waitKey(1) & 0xFF == ord('q'):         break   ### Release the video capture resource once processing is complete. cap.release()  ### Release the video writer resource to finalize and close the output video file. out.release()  ### Close all OpenCV display windows to clean up the GUI and free system resources. cv2.destroyAllWindows() 

Short summary for Part 3:
You’ve wrapped the loop with live visualization, safe exit conditions, and proper cleanup.
The result is a clean, reusable python yolov8 segmentation example that behaves like real production code, not just a quick script.

Here is the result :

FAQ :

Conclusion

YOLOv8 video segmentation gives you much more than pretty overlays. You save time using YOLOv8 for Auto-Label Segmentation
With just a handful of Python lines, you’re converting every frame into structured information: classes, masks, and exported visuals you can track, filter, and reuse.
This tutorial focused on keeping that power accessible — using a pre-trained yolov8n-seg model, a simple loop, and intuitive folder naming so anyone comfortable with basic Python can follow along.

From here, you can tune every step without rewriting the core idea.
Swap the model for a custom checkpoint, limit predictions to high-value classes, or convert the saved annotated frames into a labeled dataset for your next training run.
Because the pipeline is built on standard tools (Ultralytics YOLO and OpenCV), you can move it into notebooks, servers, or edge devices with minimal friction.

Most importantly, this yolov8 video segmentation tutorial shows a mindset: start simple, keep things readable, and let the code serve real use cases like surveillance analytics, sports breakdowns, traffic monitoring, or automatic content tagging.
Once this flow is part of your toolkit, every new video becomes an opportunity to extract meaningful, pixel-level insights instead of just frames on a timeline.

Connect :

☕ Buy me a coffee — https://ko-fi.com/eranfeit

🖥️ Email : feitgemel@gmail.com

🌐 https://eranfeit.net

🤝 Fiverr : https://www.fiverr.com/s/mB3Pbb

Enjoy,

Eran