Object detection has moved from being a research concept to a hands-on tool that anyone with a laptop can explore.

With the latest Ultralytics YOLO models, setting up an experiment is simple, and you can get detection results on your own images and videos in just a few lines of code.

In this article, I’ll walk through a notebook workflow that loads a YOLO model, runs predictions, and visualizes the outputs.

Installing and Importing the Essentials

All the code in this guide is hosted in this public GitHub Repository.

The first step is to install the Ultralytics package and bring in the necessary libraries. In a notebook, this is done with:

%pip install ultralytics --quiet 

Once installed, the imports include the YOLO class for inference, Matplotlib for visualization, and os for file handling.

from ultralytics import YOLO
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import os 

Loading a Pretrained Model

Ultralytics provides official pretrained YOLO models that are ready to use out of the box. Here I’m loading the lightweight yolo11n.pt, which is designed for fast testing. You can explore all the available pretrained models on the Ultralytics website:

# Load a model
model = YOLO("yolo11n.pt")  # load an official model 

This model is small but powerful enough to detect common objects with good accuracy.

Running Detection on an Image

With the model loaded, we can run it on a test image. In this example, the image is named car_girls.jpg and stored inside a tasks/detection/ folder.

image_name = "car_girls.jpg"

# Predict with the model
results = model(
    f"tasks/detection/{image_name}",
    save=True,
    project="tasks/detection/outputs",
) 

The save=True parameter ensures that YOLO writes the output image, with bounding boxes drawn, into the specified project folder.

Accessing and Displaying Detection Results

YOLO doesn’t just draw boxes, it also returns precise coordinates and class information for each detected object. Here’s how you can extract bounding box data and display the saved annotated image inside the notebook:

for result in results:
    xywh = result.boxes.xywh     # center-x, center-y, width, height
    xywhn = result.boxes.xywhn   # normalized
    xyxy = result.boxes.xyxy     # top-left-x, top-left-y, bottom-right-x, bottom-right-y
    xyxyn = result.boxes.xyxyn   # normalized
    names = [result.names[cls.item()] for cls in result.boxes.cls.int()]
    confs = result.boxes.conf    # confidence scores
    
    saved_path = os.path.join(result.save_dir, image_name)  # path to saved image
    
    # Load and display image
    img = mpimg.imread(saved_path)
    plt.figure(figsize=(10, 8))
    plt.imshow(img)
    plt.axis("off")
    plt.show() 

This gives both the numerical detections and a visual confirmation directly in the notebook. Learn more about the YOLO annotation format.

Extending Detection to Videos

The same workflow applies to videos. Here, I tested the model on a video file I named city_people.mp4 from Huu Huynh on Pexels.

video_name = "city_people.mp4"

# Predict with the model
results = model(
    f"tasks/detection/{video_name}",
    save=True,
    project="tasks/detection/outputs",
) 

YOLO processes each frame of the video and saves an annotated version in the output folder.

Inspecting Video Predictions

You can also inspect the detection data from video frames just as you would with images.

# Access the results
for result in results:
    xywh = result.boxes.xywh
    xywhn = result.boxes.xywhn
    xyxy = result.boxes.xyxy
    xyxyn = result.boxes.xyxyn
    names = [result.names[cls.item()] for cls in result.boxes.cls.int()]
    confs = result.boxes.conf

This provides both structured data and the path to the processed video file.

The pretrained YOLO11 model comes with a set of common object classes, but its scope is limited. If you want to detect objects outside of those predefined categories, you’ll need to fine-tune the model on your own dataset. For a practical example, see my article on fine-tuning a YOLO11 object detection model for kidney stone detection.

A Practical Takeaway

Working with YOLO inside a notebook makes object detection accessible, visual, and interactive. You can move from raw data to bounding boxes in just a few lines of code, while still having full access to the detection metadata. The same setup works whether you’re testing with a single image or running predictions on entire videos.

This workflow is not only useful for quick experimentation but also forms a foundation for more advanced projects, from dataset exploration to model fine-tuning.

Building a Construction Site Safety Monitor with YOLOv12

In this tutorial, we’ll build an automated safety monitoring system using YOLOv12, the latest version of the popular YOLO (You Only Look Once) object detection model.

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