Last Updated on 22/04/2026 by Eran Feit
Can I Track Dogs in Real-Time with YOLOv11? Instance segmentation is one of the most practical upgrades you can make when object detection alone is not enough.
In a typical computer vision workflow, object detection tells you “where” an object is, while instance segmentation tells you “exactly which pixels” belong to that object.
Modern YOLO models made instance segmentation much easier to use in Python because they bring fast inference and a clean API.
A solid instance segmentation python tutorial should help you connect the big idea to an end-to-end workflow.
Instance segmentation python tutorial, explained with a practical mindset An instance segmentation python tutorial is most useful when it focuses on what you actually need to build something working.
At a high level, instance segmentation models produce a few key outputs per object.
When you apply segmentation to video, you need to think in terms of consistency and speed.
The real target of this approach is to unlock higher-level capabilities that boxes can’t provide.
Instance segmentation explained with dogs Running Real-Time Instance Segmentation and Tracking with YOLO in Python This tutorial code is designed to show how instance segmentation can be applied to real video data using a modern YOLO segmentation model inside a clean Python workflow.
At a high level, the pipeline follows a practical computer vision loop used in many production systems.
One of the most important targets of this code is demonstrating persistent tracking combined with segmentation.
Another key goal of the tutorial is to keep the implementation readable and easy to extend.
Link to the video tutorial here .
📁 Full source code Here or here .
Master Computer Vision
Follow my latest tutorials and AI insights on my
Personal Blog .
Beginner Complete CV Bootcamp
Foundation using PyTorch & TensorFlow.
Get Started → Interactive Deep Learning with PyTorch
Hands-on practice in an interactive environment.
Start Learning → Advanced Modern CV: GPT & OpenCV4
Vision GPT and production-ready models.
Go Advanced →
YOLOv11 segmentation workflow diagram Instance Segmentation Python Tutorial Using YOLO Models Instance segmentation is one of the most powerful techniques in modern computer vision because it moves beyond simple detection and into pixel-level understanding of objects.
In a real-world computer vision pipeline, instance segmentation is especially useful when objects overlap or when shape matters more than location.
This tutorial focuses on a practical implementation of instance segmentation using Python, OpenCV, and a YOLO segmentation model.
Installing the Environment for the Instance Segmentation Python Tutorial Before running the instance segmentation python tutorial code, you need to prepare a clean Python environment with the correct CUDA, PyTorch, and Ultralytics versions.
The installation process includes creating a Conda environment, installing PyTorch with CUDA support, and installing Ultralytics YOLO and OpenCV.
This setup is designed to match real-world development environments used in computer vision projects.
### Create a new Conda environment for the project conda create -- name YoloV11 - 311 python = 3.11 ### Activate the Conda environment conda activate YoloV11 - 311 ### Check CUDA installation version nvcc -- version ### Install PyTorch with CUDA 12.4 support conda install pytorch == 2.5 . 0 torchvision == 0.20 . 0 torchaudio == 2.5 . 0 pytorch - cuda = 12.4 - c pytorch - c nvidia ### Install Ultralytics YOLO library pip install ultralytics == 8.3 . 59 ### Install OpenCV for video processing pip install opencv - python == 4.10 . 0 . 84 Summary:
Loading the segmentation model After the environment is ready, the next step is importing the required libraries and loading the pretrained segmentation model.
### Import OpenCV for video reading, writing, and visualization import cv2 ### Import YOLO model class from Ultralytics for segmentation inference from ultralytics import YOLO ### Import annotation utilities for drawing masks and labels from ultralytics . utils . plotting import Annotator , colors ### Load a pretrained YOLOv11 segmentation model model = YOLO ( " yolo11l-seg.pt " ) Summary:
Opening the video stream and preparing output video writer Once the model is loaded, the next step is preparing the input video stream and output recording pipeline.
This structure is very common in real-time computer vision systems.
To make this instance segmentation python tutorial easier to follow, you can use the same video file that is used inside the code examples.
If you want access to the exact video file used in this tutorial, you can request it directly by email.
When requesting the video file, include a short message mentioning the tutorial title so it is easier to identify your request.
To request the tutorial video file, send an email to: feitgemel@gmail.com
### Open the input video file for frame-by-frame processing cap = cv2 . VideoCapture ( " Best-Semantic-Segmentation-models/Yolo-V11/Segmentation and Tracking using YoloV11/dogs.mp4 " ) ### Extract video width, height, and FPS from the input video w , h , fps = ( int ( cap . get ( x )) for x in ( cv2 . CAP_PROP_FRAME_WIDTH , cv2 . CAP_PROP_FRAME_HEIGHT , cv2 . CAP_PROP_FPS )) ### Create a VideoWriter object to save processed output video out = cv2 . VideoWriter ( " Best-Semantic-Segmentation-models/Yolo-V11/Segmentation and Tracking using YoloV11/output_video.avi " , cv2 . VideoWriter_fourcc ( * " MJPG " ), fps , ( w , h ) ) Summary:
Running real-time segmentation and tracking on video frames This is the core of the instance segmentation python tutorial.
Tracking combined with segmentation is extremely powerful.
### Start processing frames in a continuous loop while True : ### Read the next frame from the video stream ret , img = cap . read () ### Check if frame reading failed or video ended if not ret : print ( " Video ended or error reading video " ) break ### Create annotation helper object for drawing masks and labels annotator = Annotator ( img , line_width = 2 ) ### Run segmentation tracking inference on the current frame results = model . track ( img , persist =True ) ### Check if segmentation masks and tracking boxes exist if results [ 0 ]. boxes is not None and results [ 0 ]. masks is not None : ### Extract segmentation polygon masks masks = results [ 0 ]. masks . xy ### Extract tracking IDs for each detected object track_ids = results [ 0 ]. boxes . id . int (). cpu (). tolist () ### Loop through masks and track IDs together for mask , track_id in zip ( masks , track_ids ): ### Generate consistent color based on track ID color = colors ( int ( track_id ) , True ) ### Select readable text color for labels txt_color = annotator . get_txt_color ( color ) ### Draw segmentation mask and bounding overlay with tracking ID label annotator . seg_bbox ( mask = mask , mask_color = color , label = str ( track_id ) , txt_color = txt_color ) Summary:
Saving processed frames and handling display and cleanup After segmentation results are drawn, frames are saved into the output video and displayed in real time.
Proper cleanup is critical in video processing applications.
### Write processed frame into output video file out . write ( img ) ### Display the processed frame in a preview window cv2 . imshow ( " Result " , img ) ### Allow exit if user presses Q key if cv2 . waitKey ( 1 ) & 0x FF == ord ( ' q ' ): break ### Release output video writer out . release () ### Release video capture object cap . release () ### Close all OpenCV display windows cv2 . destroyAllWindows () ### Print completion message after processing finishes print ( " Video processing completed and saved to output_video.mp4 " ) Summary:
FAQ — Instance Segmentation Python Tutorial What is instance segmentation? Instance segmentation detects objects and generates pixel-level masks for each object.
Why is segmentation better than detection? Segmentation provides precise object boundaries instead of simple bounding boxes.
Conclusion Instance segmentation represents one of the most advanced yet practical techniques available in modern computer vision.
This tutorial demonstrated how instance segmentation can be implemented using a clean and efficient Python workflow.
The real power of this workflow is flexibility.
As computer vision continues evolving, segmentation will become even more central to intelligent systems.
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
Skip to content 
