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PyCharm Docs Inspect fixes (#18432)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-12-29 23:19:05 +05:00 • committed by GitHub
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docs/en/integrations/ncnn.md
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@ -101,7 +101,7 @@ For more details about supported export options, visit the [Ultralytics document
## Deploying Exported YOLO11 NCNN Models
After successfully exporting your Ultralytics YOLO11 models to NCNN format, you can now deploy them. The primary and recommended first step for running a NCNN model is to utilize the YOLO("./model_ncnn_model") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your NCNN models in various other settings, take a look at the following resources:
After successfully exporting your Ultralytics YOLO11 models to NCNN format, you can now deploy them. The primary and recommended first step for running a NCNN model is to utilize the YOLO("yolo11n_ncnn_model/") method, as outlined in the previous usage code snippet. However, for in-depth instructions on deploying your NCNN models in various other settings, take a look at the following resources:
- **[Android](https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-android)**: This blog explains how to use NCNN models for performing tasks like [object detection](https://www.ultralytics.com/glossary/object-detection) through Android applications.