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Update more/missing type qualifiers to lowercase MkDocs admonitions (#16215)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Jan Knobloch 2024-09-11 18:30:13 +02:00 • committed by GitHub
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docs/en/modes/predict.md
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@ -100,7 +100,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
YOLOv8 can process different types of input sources for inference, as shown in the table below. The sources include static images, video streams, and various data formats. The table also indicates whether each source can be used in streaming mode with the argument `stream=True` ✅. Streaming mode is beneficial for processing videos or live streams as it creates a generator of results instead of loading all frames into memory.
!!! tip "Tip"
!!! tip
Use `stream=True` for processing long videos or large datasets to efficiently manage memory. When `stream=False`, the results for all frames or data points are stored in memory, which can quickly add up and cause out-of-memory errors for large inputs. In contrast, `stream=True` utilizes a generator, which only keeps the results of the current frame or data point in memory, significantly reducing memory consumption and preventing out-of-memory issues.