Last Updated on 29/04/2026 by Eran Feit
Understanding a visual scene requires more than just drawing boxes around cars; it requires identifying every pixel, from the individual vehicles to the road and sky. In this Detectron2 Panoptic Segmentation Python Tutorial , you will solve the complex problem of ‘complete scene understanding.’ While instance segmentation tracks objects (things) and semantic segmentation labels regions (stuff), panoptic segmentation merges them into a single, unified output. By the end of this guide, you’ll have a production-ready Python script that leverages Facebook AI Research’s (FAIR) Detectron2 to perform pixel-perfect analysis on any image.
What you will learn in this tutorial:
The core differences between panoptic and standard segmentation. How to load and configure pre-trained Detectron2 models. Step-by-step Python implementation for real-world images. Pro-tips for optimizing inference speed. Why Panoptic Segmentation Matters in Computer Vision Detectron2 Panoptic Segmentation Made Easy for Beginners 10 Panoptic segmentation sounds like a big research term, but the goal is actually very intuitive: understand the entire scene, pixel by pixel.
Most people start with two related ideas in computer vision:
Semantic segmentation : label every pixel with a class.Instance segmentation : find each object separately. Panoptic segmentation gives you both at the same time.
Assigning a semantic label to every single pixel (road, sky, wall, grass, etc.). Separating out each distinct object instance (Person 1 vs Person 2, Car 1 vs Car 2, etc.). So instead of just saying “there are cars here,” panoptic segmentation can tell you:
exactly which pixels belong to Car 1, which belong to Car 2, which pixels are road, which pixels are sidewalk, which pixels are building, and which pixels are sky. In other words, panoptic segmentation answers two questions at once:
“What is everything in this image?” “Where exactly is each thing in the image?” That’s why it’s so valuable in robotics, autonomous driving, retail analytics, safety zones, sports analytics, medical imaging, and more.
For another hands-on look at segmentation in real projects, you can also check out my full U-Net medical segmentation walkthrough in TensorFlow and Keras, where I segment polyps in colonoscopy images step by step: https://eranfeit.net/u-net-medical-segmentation-with-tensorflow-and-keras-polyp-segmentation/
Where Detectron2 comes in Now let’s connect this to Detectron2.
Detectron2 is a computer vision framework from Meta AI (FAIR) that gives you high-quality pretrained models for tasks like object detection, instance segmentation, semantic segmentation — and yes, panoptic segmentation.
What makes Detectron2 powerful for panoptic segmentation is that you don’t have to build or train a giant neural network yourself.
find each individual object instance (people, cars, bikes, etc.), label broad background regions (road, sidewalk, sky, building), and output one unified map where every pixel in the image is explained. This is sometimes called “things and stuff.”
Why is that a big deal for beginners?
Because normally, to get a result like that, you’d have to:
collect a labeled dataset, train a segmentation model, tune it, and write a lot of post-processing code to merge semantic masks and instance masks. With Detectron2 panoptic segmentation, you skip all of that.
load a pretrained panoptic model config, load its pretrained weights, run inference on your own image, and visualize the result. That’s it.
Even better, you can force Detectron2 to run on CPU.
So when we say “Detectron2 Panoptic Segmentation Made Easy for Beginners,” this is what we mean:
If you’re more into real-time object detection instead of full-scene pixel mapping, I also have a Jetson Nano tutorial where I classify objects directly from a live camera feed using Python and OpenCV: https://eranfeit.net/how-to-classify-objects-in-live-camera-using-jetson-nano/
Link for the video tutorial : https://youtu.be/MuzNooUNZSY
You can download the code here : https://eranfeit.lemonsqueezy.com/buy/86c74809-3fc4-4f87-bd5f-9ae0ecf044e2
or here : https://ko-fi.com/s/7317da4e07
Link to Medium blog post here .
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Why Use Detectron2 for Panoptic Segmentation? Detectron2, built on PyTorch, provides a high-performance baseline for various object detection and segmentation tasks. For a Detectron2 panoptic segmentation tutorial , we specifically look at how the library handles “things” (countable objects like cars or people) and “stuff” (amorphous regions like sky or grass) simultaneously.
This is particularly useful in autonomous driving and robotics, where the system needs to recognize not just the obstacles (cars) but also the context of the environment (the road and sidewalk).
Getting the image ready so Detectron2 can analyze it Introduction to this part
Before we run any AI model, we need an image in memory.cv2) to read an image from disk.
In this section we also import all the libraries we’ll use.
Resizing is not just about speed.
This step is important for beginners because it makes the project run smoothly even without a GPU.
Elaborated description of what happens in the code
We import core libraries.cv2.imread().scale_precent.cv2.resize() with an interpolation mode that is good for shrinking.myNewImage contains the resized frame we’ll feed into Detectron2.
If you want a simpler approach that doesn’t even need deep learning weights, I also wrote about K-Means image segmentation in OpenCV, where we cluster pixel colors to split regions: https://eranfeit.net/python-image-segmentation-made-easy-with-opencv-and-k-means-algorithm/
Here is our test image :
Detectron2 Panoptic Segmentation Made Easy for Beginners 11 ### Import core PyTorch. import torch ### Import Detectron2 main package so we can access its vision tools. import detectron2 ### Import NumPy for array operations that support OpenCV and general math. import numpy as np ### Import OpenCV for reading, resizing, showing, and saving images. import cv2 ### Bring in Detectron2’s model zoo for pretrained configs and weights. from detectron2 import model_zoo ### Import DefaultPredictor, which wraps model loading and inference. from detectron2 . engine import DefaultPredictor ### get_cfg gives us a fresh Detectron2 config we can modify. from detectron2 . config import get_cfg ### Visualizer draws segmentation masks, labels, and colors on top of images. from detectron2 . utils . visualizer import Visualizer ### MetadataCatalog stores dataset metadata (class names, colors) used by Visualizer. from detectron2 . data import MetadataCatalog ### Path to the image we want to segment. imagePath = " pexels-brett-sayles-1115171.jpg " ### Read the image from disk into memory as a NumPy array (BGR color order). myNewImage = cv2 . imread ( imagePath ) ### Choose how much to scale down the image (percentage of original size). scale_precent = 30 ### Compute new width based on original width * scale_precent / 100. width = int ( myNewImage . shape [ 1 ] * scale_precent / 100 ) ### Compute new height based on original height * scale_precent / 100. height = int ( myNewImage . shape [ 0 ] * scale_precent / 100 ) ### Create a size tuple in (width, height) order for OpenCV. dim = ( width , height ) ### Resize the original image to the new dimensions using INTER_AREA (good for shrinking). myNewImage = cv2 . resize ( myNewImage , dim , interpolation = cv2 . INTER_AREA ) Summary of this part :
Resizing the input image isn’t just about saving screen real estate. In deep learning inference, the memory (VRAM) consumption scales with image resolution. By downscaling to 30%, we significantly reduce the computational load, allowing the model to run efficiently even on standard CPU environments without sacrificing the structural integrity needed for accurate segmentation
We imported everything we need for vision and segmentation.myNewImage) ready for Detectron2 to analyze in the next step.
Loading a pretrained panoptic model from Detectron2 Introduction to this part
This section is where Detectron2 really shines for beginners.
We’ll create a config object using get_cfg().COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml.
We also point the config to the matching pretrained weights using model_zoo.get_checkpoint_url().
Finally, we tell Detectron2 to run on "cpu".
Elaborated description of what happens in the code
We create cfg_pan = get_cfg().cfg_pan.merge_from_file(...) to inject all the model details into our config.cfg_pan.MODEL.WEIGHTS so the predictor knows which trained weights to load.cfg_pan.MODEL.DEVICE = "cpu" so inference stays on the CPU.DefaultPredictor(cfg_pan) object, which bundles preprocessing and inference logic for us.
After this block, we have a predictor that can generate panoptic segmentation with one line: predictor(myNewImage).
### Create a new Detectron2 config object. cfg_pan = get_cfg () ### Load the predefined panoptic segmentation config from the model zoo. cfg_pan . merge_from_file ( model_zoo . get_config_file ( " COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml " )) ### Set the pretrained weights for that exact config so we don't have to train anything. cfg_pan . MODEL . WEIGHTS = model_zoo . get_checkpoint_url ( " COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml " ) ### Force Detectron2 to run on CPU. ### If you have CUDA available, you can remove this line or set it to "cuda". cfg_pan . MODEL . DEVICE = " cpu " # if you have Cuda , dont need this line ### Build a DefaultPredictor. ### This object handles preprocessing, running the model, and postprocessing. predictor = DefaultPredictor ( cfg_pan ) Summary of this part :
The choice of COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml is strategic. The ‘R_101’ refers to a ResNet-101 backbone, which provides a deeper feature extraction layers compared to ResNet-50. This is crucial for panoptic tasks where the model must simultaneously distinguish between discrete objects like ‘pedestrians’ and amorphous regions like ‘sidewalks’ using a Feature Pyramid Network (FPN).
We prepared a Detectron2 panoptic segmentation model using a known config and pretrained weights.DefaultPredictor, so inference becomes a single function call.
If you want to focus on classification instead of segmentation, you can read my TensorFlow image classification tutorial where I compare ResNet50 and MobileNet, explain training, and walk through evaluation: https://eranfeit.net/tensorflow-image-classification-tutorial-resnet50-vs-mobilenet/
Running panoptic segmentation and drawing the results Introduction to this part
Now we’ll actually run inference.predictor(myNewImage), Detectron2 returns a dictionary of outputs."panoptic_seg", which itself contains two important pieces.segments_info, a structure that tells us which class each segment belongs to.
After we get those results, we use Visualizer to create a color overlay.MetadataCatalog.
Elaborated description of what happens in the code
We call the predictor and unpack:panoptic_seg, segments_info = predictor(myNewImage)["panoptic_seg"].
We create a Visualizer with:
The image in RGB order (OpenCV uses BGR, so we reverse the last channel order [:, :, ::-1]). The training dataset metadata from cfg_pan. A scale parameter for how large to draw elements. We then ask Visualizer to draw panoptic predictions..get_image() method gives us the final colorized image.
The Visualizer class in Detectron2 is a powerful debugging tool. Unlike standard OpenCV drawing functions, it automatically handles the ‘MetadataCatalog’ to map class IDs to human-readable labels and distinct colors. This ensures that your ‘thing’ detections (instances) and ‘stuff’ segments (background) are visually distinguishable in the final output.
### Run the predictor on our resized image to get panoptic segmentation output. panoptic_seg , segments_info = predictor ( myNewImage )[ " panoptic_seg " ] ### Create a Visualizer that can draw masks, labels, and colors. ### Note: We convert BGR (OpenCV) to RGB (Visualizer expects RGB). v = Visualizer ( myNewImage [:, :, :: - 1 ], MetadataCatalog . get ( cfg_pan . DATASETS . TRAIN [ 0 ]), scale = 1.0 ) ### Ask the Visualizer to draw the full panoptic segmentation (things + stuff). out = v . draw_panoptic_seg_predictions ( panoptic_seg . to ( " cpu " ), segments_info ) ### Convert the visualized result back to BGR so OpenCV can display and save it correctly. img = out . get_image ()[:, :, :: - 1 ] Summary of this part
We ran Detectron2 panoptic segmentation on our image and generated a pixel-accurate scene map.img, which is now a human-readable overlay.
Previewing and saving your segmented scene Introduction to this part
Once we have the result, we want to see it and keep it.panoptic.png.
This is the “proof moment.”
Elaborated description of what happens in the code
cv2.imshow("img", myNewImage) opens a window called “img” with the resized original frame.cv2.imshow("predict", img) opens a window called “predict” with the panoptic overlay.cv2.imwrite("e:/temp/panoptic.png", img) saves the overlay to disk so you can reuse it in reports, blog posts, or presentations.cv2.waitKey(0) pauses the script until you press a key, so the windows don’t disappear immediately.
You can also skip the imshow steps and just keep imwrite if you’re running on a server without a display.
### Show the original resized image in a window called "img". cv2 . imshow ( " img " , myNewImage ) ### Show the panoptic segmentation result in a window called "predict". cv2 . imshow ( " predict " , img ) ### Save the segmentation overlay to disk so you can use it later. cv2 . imwrite ( " e:/temp/panoptic.png " , img ) ### Wait for a key press so the OpenCV windows stay open. cv2 . waitKey ( 0 ) Summary of this part
We previewed both the input and the output.
The result : Detectron2 Panoptic Segmentation Made Easy for Beginners 12 FAQ : What is panoptic segmentation? Panoptic segmentation labels every pixel in the image. It combines semantic segmentation (stuff like road or sky) and instance segmentation (separate objects like Person 1 vs Person 2).
Why are we using Detectron2? Detectron2 gives us pretrained, production-grade vision models. We can run panoptic segmentation with just a few lines of Python, no custom training required.
Can I run this on CPU? Yes. Setting cfg_pan.MODEL.DEVICE = “cpu” forces inference on CPU so you can test without a GPU. It’s slower, but it works for demos and screenshots.
Which model are we loading? We’re using a COCO panoptic model from the Detectron2 model zoo (panoptic_fpn_R_101_3x.yaml). It’s already trained to recognize common objects and background classes.
What does segments_info mean? segments_info tells us what each region in the prediction represents. It includes the object category and whether it’s a ‘thing’ (like a person) or ‘stuff’ (like sky or road).
Why do we resize the input image? Resizing to 30% cuts down the number of pixels. With fewer pixels, inference runs faster, especially on CPU, and the visualizer stays responsive.
Can this run on video? Yes. You can loop through each frame of a video, run predictor(frame), draw the panoptic overlay, and export the processed frames as a new video file.
Can I save results without opening windows? Absolutely. You can skip cv2.imshow and just call cv2.imwrite to store the visualized output as an image file.
How is this different from bounding boxes? Bounding boxes only draw rectangles around objects. Panoptic segmentation creates pixel-accurate masks for each object and also labels the background areas in the scene.
What should I learn next? The next step is custom training. You can register your own dataset in Detectron2 and fine-tune panoptic segmentation for your specific classes and environment.
Conclusion — how to move from demo to real projects You now have a working panoptic segmentation pipeline that is friendly for beginners.
This approach shows why panoptic segmentation is so useful.
You also saw how Detectron2 hides a lot of internal complexity.
From here, you can extend this in a few directions.
The most important part is that you are no longer guessing what panoptic segmentation is.
Expert Tips for Troubleshooting Detectron2 During my implementation of this Detectron2 panoptic segmentation tutorial , I found that two common issues often frustrate beginners:
CUDA Compatibility: Always ensure your PyTorch version matches your CUDA toolkit version, or the library will fail to utilize your GPU.Model Zoo Weights: If your output looks “messy,” double-check that you are using the COCO-PanopticSegmentation configuration and not a standard Mask R-CNN config. Frequently Asked Questions Q: Is Detectron2 faster than Mask R-CNN? A: Detectron2 is an engine that implements Mask R-CNN. It is significantly more optimized than the original Mask R-CNN implementations, offering faster training and inference times.
Q: Can I run this on a CPU? A: Yes, but it is much slower. For real-time applications, a GPU with at least 8GB of VRAM is recommended.
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