DOCS . ULTRALYTICS . COM {}

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We are analyzing https://docs.ultralytics.com/modes/predict/.

Title:
Model Prediction with Ultralytics YOLO - Ultralytics YOLO Docs
Description:
Harness the power of Ultralytics YOLO11 for real-time, high-speed inference on various data sources. Learn about predict mode, key features, and practical applications.
Website Age:
11 years and 4 months (reg. 2014-02-13).

Matching Content Categories {📚}

Photography
Video & Online Content
Mobile Technology & AI

Content Management System {📝}

What CMS is docs.ultralytics.com built with?

Custom-built

No common CMS systems were detected on Docs.ultralytics.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of docs.ultralytics.com audience?

🌟 Strong Traffic: 100k - 200k visitors per month

Based on our best estimate, this website will receive around 100,019 visitors per month in the current month.
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How Does Docs.ultralytics.com Make Money? {💸}

We don’t know how the website earns money.

Websites don't always need to be profitable; some serve as platforms for education or personal expression. Websites can serve multiple purposes. And this might be one of them. Docs.ultralytics.com has a secret sauce for making money, but we can't detect it yet.

Keywords {🔍}

results, inference, yolo, image, boxes, predict, model, object, ultralytics, method, detection, video, bool, masks, str, format, keypoints, class, torchtensor, returns, property, memory, file, images, objects, return, run, false, mode, probs, list, frames, numpy, type, true, videos, data, import, obb, save, path, description, cpu, arguments, streaming, bounding, size, sources, source, device,

Topics {✒️}

multi-class detection tasks performs thread-safe prediction multi-class detection scenarios thread-safe inference guarantees real-world applications manufacturing employ thread-local storage yolo predict source=video yolo predict source=image top previous val enables test-time augmentation perform high-speed inference multi-threaded application providing real-time feedback pil import image real-time video streams streaming modes real-time object detection enables memory-efficient processing ultralytics yolo model multi picture object youtube video thread-safe inference ultralytics yolo support webcam ✅ real-time applications threading import thread multi-stream ✅ pil image object yolo11n-pose downstream tasks memory-efficient python generator ultralytics yolo11 offers bgr channels uint8 creating focused datasets glob pattern mpeg transport stream real-time inference model instance thread audio video interleave faster mask plotting rgb channels float32 stream=true utilizes oriented bounding boxes optimize inference speed football player detection people fall detection automatically adjusted based avoiding race conditions enables class-agnostic single model inside

Questions {❓}

How can I run inference using Ultralytics YOLO on different data sources?
How can I visualize and save the results of YOLO predictions?
How do I optimize YOLO inference speed and memory usage?
What inference arguments does Ultralytics YOLO support?
What is Ultralytics YOLO and its predict mode for real-time inference?
Why Use Ultralytics YOLO for Inference?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:Predict
      image:
         https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-ecosystem-integrations.avif
      datePublished:2023-11-12 02:49:37 +0100
      dateModified:2025-06-30 14:21:43 +0500
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Harness the power of Ultralytics YOLO11 for real-time, high-speed inference on various data sources. Learn about predict mode, key features, and practical applications.
      mainEntity:
            type:Question
            name:What is Ultralytics YOLO and its predict mode for real-time inference?
            acceptedAnswer:
               type:Answer
               text:Ultralytics YOLO is a state-of-the-art model for real-time object detection, segmentation, and classification. Its predict mode allows users to perform high-speed inference on various data sources such as images, videos, and live streams. Designed for performance and versatility, it also offers batch processing and streaming modes. For more details on its features, check out the Ultralytics YOLO predict mode.
            type:Question
            name:How can I run inference using Ultralytics YOLO on different data sources?
            acceptedAnswer:
               type:Answer
               text:Ultralytics YOLO can process a wide range of data sources, including individual images, videos, directories, URLs, and streams. You can specify the data source in the model.predict() call. For example, use 'image.jpg' for a local image or 'https://ultralytics.com/images/bus.jpg' for a URL. Check out the detailed examples for various inference sources in the documentation.
            type:Question
            name:How do I optimize YOLO inference speed and memory usage?
            acceptedAnswer:
               type:Answer
               text:To optimize inference speed and manage memory efficiently, you can use the streaming mode by setting stream=True in the predictor's call method. The streaming mode generates a memory-efficient generator of Results objects instead of loading all frames into memory. For processing long videos or large datasets, streaming mode is particularly useful. Learn more about streaming mode.
            type:Question
            name:What inference arguments does Ultralytics YOLO support?
            acceptedAnswer:
               type:Answer
               text:The model.predict() method in YOLO supports various arguments such as conf, iou, imgsz, device, and more. These arguments allow you to customize the inference process, setting parameters like confidence thresholds, image size, and the device used for computation. Detailed descriptions of these arguments can be found in the inference arguments section.
            type:Question
            name:How can I visualize and save the results of YOLO predictions?
            acceptedAnswer:
               type:Answer
               text:After running inference with YOLO, the Results objects contain methods for displaying and saving annotated images. You can use methods like result.show() and result.save(filename="result.jpg") to visualize and save the results. For a comprehensive list of these methods, refer to the working with results section.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:What is Ultralytics YOLO and its predict mode for real-time inference?
      acceptedAnswer:
         type:Answer
         text:Ultralytics YOLO is a state-of-the-art model for real-time object detection, segmentation, and classification. Its predict mode allows users to perform high-speed inference on various data sources such as images, videos, and live streams. Designed for performance and versatility, it also offers batch processing and streaming modes. For more details on its features, check out the Ultralytics YOLO predict mode.
      name:How can I run inference using Ultralytics YOLO on different data sources?
      acceptedAnswer:
         type:Answer
         text:Ultralytics YOLO can process a wide range of data sources, including individual images, videos, directories, URLs, and streams. You can specify the data source in the model.predict() call. For example, use 'image.jpg' for a local image or 'https://ultralytics.com/images/bus.jpg' for a URL. Check out the detailed examples for various inference sources in the documentation.
      name:How do I optimize YOLO inference speed and memory usage?
      acceptedAnswer:
         type:Answer
         text:To optimize inference speed and manage memory efficiently, you can use the streaming mode by setting stream=True in the predictor's call method. The streaming mode generates a memory-efficient generator of Results objects instead of loading all frames into memory. For processing long videos or large datasets, streaming mode is particularly useful. Learn more about streaming mode.
      name:What inference arguments does Ultralytics YOLO support?
      acceptedAnswer:
         type:Answer
         text:The model.predict() method in YOLO supports various arguments such as conf, iou, imgsz, device, and more. These arguments allow you to customize the inference process, setting parameters like confidence thresholds, image size, and the device used for computation. Detailed descriptions of these arguments can be found in the inference arguments section.
      name:How can I visualize and save the results of YOLO predictions?
      acceptedAnswer:
         type:Answer
         text:After running inference with YOLO, the Results objects contain methods for displaying and saving annotated images. You can use methods like result.show() and result.save(filename="result.jpg") to visualize and save the results. For a comprehensive list of these methods, refer to the working with results section.
Answer:
      text:Ultralytics YOLO is a state-of-the-art model for real-time object detection, segmentation, and classification. Its predict mode allows users to perform high-speed inference on various data sources such as images, videos, and live streams. Designed for performance and versatility, it also offers batch processing and streaming modes. For more details on its features, check out the Ultralytics YOLO predict mode.
      text:Ultralytics YOLO can process a wide range of data sources, including individual images, videos, directories, URLs, and streams. You can specify the data source in the model.predict() call. For example, use 'image.jpg' for a local image or 'https://ultralytics.com/images/bus.jpg' for a URL. Check out the detailed examples for various inference sources in the documentation.
      text:To optimize inference speed and manage memory efficiently, you can use the streaming mode by setting stream=True in the predictor's call method. The streaming mode generates a memory-efficient generator of Results objects instead of loading all frames into memory. For processing long videos or large datasets, streaming mode is particularly useful. Learn more about streaming mode.
      text:The model.predict() method in YOLO supports various arguments such as conf, iou, imgsz, device, and more. These arguments allow you to customize the inference process, setting parameters like confidence thresholds, image size, and the device used for computation. Detailed descriptions of these arguments can be found in the inference arguments section.
      text:After running inference with YOLO, the Results objects contain methods for displaying and saving annotated images. You can use methods like result.show() and result.save(filename="result.jpg") to visualize and save the results. For a comprehensive list of these methods, refer to the working with results section.

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External Links {🔗}(53)

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