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Add FAQ sections to Modes and Tasks (#14181)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Abirami Vina <abirami.vina@gmail.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
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Glenn Jocher 2024-07-04 17:16:16 +02:00 • committed by GitHub
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@ -800,3 +800,25 @@ This script will run predictions on each frame of the video, visualize the resul
[car spare parts]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a0f802a8-0776-44cf-8f17-93974a4a28a1
[football player detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d320e1f-fc57-4d7f-a691-78ee579c3442
[human fall detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/86437c4a-3227-4eee-90ef-9efb697bdb43
## FAQ
### What is Ultralytics YOLOv8 and its predict mode for real-time inference?
Ultralytics YOLOv8 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 YOLOv8 predict mode](#key-features-of-predict-mode).
### How can I run inference using Ultralytics YOLOv8 on different data sources?
Ultralytics YOLOv8 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](#inference-sources) in the documentation.
### How do I optimize YOLOv8 inference speed and memory usage?
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](#key-features-of-predict-mode).
### What inference arguments does Ultralytics YOLOv8 support?
The `model.predict()` method in YOLOv8 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](#inference-arguments) section.
### How can I visualize and save the results of YOLOv8 predictions?
After running inference with YOLOv8, 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](#working-with-results) section.