Analytics using Ultralytics YOLO26

Analytics with Ultralytics YOLO26 turns object detection and tracking results into real-time charts, so you can watch how object counts change across a video frame by frame. This guide covers four data visualization types — line graphs, bar plots, pie charts, and area plots — and shows how to switch between them with shared Python and CLI examples.

<strong>Watch:</strong> How to generate Analytical Graphs using Ultralytics | Line Graphs, Bar Plots, Area and Pie Charts

Visual Samples

Why Visualize Detection Data?

• Line graphs are ideal for tracking changes over short and long periods and for comparing changes for multiple groups over the same period.
• Bar plots are suitable for comparing quantities across different categories and showing relationships between a category and its numerical value.
• Pie charts are effective for illustrating proportions among categories and showing parts of a whole.
• Area plots fill the line graph so per-class object counts over time are easier to read at a glance.

Generate Analytics Graphs

Pass your video to the Analytics solution and select a chart with analytics_type. The solution runs detection and tracking on every frame and renders a 1280×720 chart (by default) you can write straight to an output video. Switch between "line", "bar", "pie", and "area" with a single argument.

!!! example "Analytics using Ultralytics YOLO"

=== "CLI"

    ```bash
    yolo solutions analytics show=True

    # Pass the source
    yolo solutions analytics source="path/to/video.mp4"

    # Generate the pie chart
    yolo solutions analytics analytics_type="pie" show=True

    # Generate the bar plots
    yolo solutions analytics analytics_type="bar" show=True

    # Generate the area plots
    yolo solutions analytics analytics_type="area" show=True
    ```

=== "Python"

    ```python
    import cv2

    from ultralytics import solutions

    cap = cv2.VideoCapture("path/to/video.mp4")
    assert cap.isOpened(), "Error reading video file"

    # Video writer
    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(
        "analytics_output.avi",
        cv2.VideoWriter_fourcc(*"MJPG"),
        fps,
        (1280, 720),  # this is fixed
    )

    # Initialize analytics object
    analytics = solutions.Analytics(
        show=True,  # display the output
        analytics_type="line",  # pass the analytics type, could be "pie", "bar" or "area".
        model="yolo26n.pt",  # path to the YOLO26 model file
        # classes=[0, 2],  # display analytics for specific detection classes
    )

    # Process video
    frame_count = 0
    while cap.isOpened():
        success, im0 = cap.read()
        if success:
            frame_count += 1
            results = analytics(im0, frame_count)  # update analytics graph every frame

            # print(results)  # access the output

            out.write(results.plot_im)  # write the video file
        else:
            break

    cap.release()
    out.release()
    cv2.destroyAllWindows()  # destroy all opened windows
    ```

Analytics Arguments

Here's a table outlining the Analytics arguments:

{% from "macros/solutions-args.md" import param_table %} {{ param_table(["model", "analytics_type"]) }}

You can also leverage different track arguments in the Analytics solution.

{% from "macros/track-args.md" import param_table %} {{ param_table(["tracker", "conf", "iou", "classes", "verbose", "device"]) }}

Additionally, the following visualization arguments are supported:

{% from "macros/visualization-args.md" import param_table %} {{ param_table(["show", "line_width"]) }}

Conclusion

Understanding when and how to use different types of visualizations is crucial for effective data analysis. Line graphs, bar plots, and pie charts are fundamental tools that can help you convey your data's story more clearly and effectively. The Ultralytics YOLO26 Analytics solution provides a streamlined way to generate these visualizations from your object detection and tracking results, making it easier to extract meaningful insights from your visual data.

FAQ

How do I create a line graph using Ultralytics YOLO26 Analytics?

To create a line graph using Ultralytics YOLO26 Analytics, follow these steps:

1. Load a YOLO26 model and open your video file.
2. Initialize the Analytics class with analytics_type="line".
3. Iterate through video frames, calling the solution each frame to update the line graph with data such as object counts.
4. Write results.plot_im to an output video to save the chart.

Use the Python example above as a starting point — it already runs the full frame loop, and a line graph is the default analytics_type.

What are the benefits of using Ultralytics YOLO26 for creating bar plots?

Using Ultralytics YOLO26 for creating bar plots offers several benefits:

1. Real-time Data Visualization: Seamlessly integrate object detection results into bar plots for dynamic updates.
2. Ease of Use: Simple API and functions make it straightforward to implement and visualize data.
3. Customization: Customize titles, labels, colors, and more to fit your specific requirements.
4. Efficiency: Efficiently handle large amounts of data and update plots in real-time during video processing.

To generate a bar plot, set analytics_type="bar" in the Python example above — the rest of the frame loop is identical. See the Visual Samples section for a preview.

Why should I use Ultralytics YOLO26 for creating pie charts in my data visualization projects?

Ultralytics YOLO26 is an excellent choice for creating pie charts because:

1. Integration with Object Detection: Directly integrate object detection results into pie charts for immediate insights.
2. User-Friendly API: Simple to set up and use with minimal code.
3. Customizable: Various customization options for colors, labels, and more.
4. Real-time Updates: Handle and visualize data in real-time, which is ideal for video analytics projects.

To generate a pie chart, set analytics_type="pie" in the Python example above. For more information, refer to the Visual Samples section in the guide.

Can Ultralytics YOLO26 be used to track objects and dynamically update visualizations?

Yes. Tracking is built into the Analytics solution: it tracks multiple objects in real time and updates the chart from the tracked objects' data every frame, so line graphs, bar plots, pie charts, and area plots all reflect live counts. This is exactly what the frame loop in the Python example above does. To learn about the underlying tracking functionality, see the Tracking section.

What makes Ultralytics YOLO26 different from other object detection solutions like OpenCV and TensorFlow?

Ultralytics YOLO26 stands out from other object detection solutions like OpenCV and TensorFlow for multiple reasons:

1. State-of-the-art Accuracy: YOLO26 provides superior accuracy in object detection, instance segmentation, semantic segmentation, and classification tasks.
2. Ease of Use: User-friendly API allows for quick implementation and integration without extensive coding.
3. Real-time Performance: Optimized for high-speed inference, suitable for real-time applications.
4. Diverse Applications: Supports various tasks including multi-object tracking, custom model training, and exporting to different formats like ONNX, TensorRT, and CoreML.
5. Comprehensive Documentation: Extensive documentation and blog resources to guide users through every step.

For more detailed comparisons and use cases, explore our Ultralytics Blog.