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Moved IMX under Deployment section (#18742)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Francesco Mattioli 2025-01-18 18:28:30 +01:00 • committed by GitHub
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docs/en/integrations/index.md
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@ -61,8 +61,6 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
- [Albumentations](albumentations.md): Enhance your Ultralytics models with powerful image augmentations to improve model robustness and generalization.
- [SONY IMX500](sony-imx500.md): Optimize and deploy [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) models on Raspberry Pi AI Cameras with the IMX500 sensor for fast, low-power performance.
## Deployment Integrations
- [CoreML](coreml.md): CoreML, developed by [Apple](https://www.apple.com/), is a framework designed for efficiently integrating machine learning models into applications across iOS, macOS, watchOS, and tvOS, using Apple's hardware for effective and secure [model deployment](https://www.ultralytics.com/glossary/model-deployment).
@ -95,6 +93,8 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
- [TorchScript](torchscript.md): Developed as part of the [PyTorch](https://pytorch.org/) framework, TorchScript enables efficient execution and deployment of machine learning models in various production environments without the need for Python dependencies.
- [SONY IMX500](sony-imx500.md): Optimize and deploy [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) models on Raspberry Pi AI Cameras with the IMX500 sensor for fast, low-power performance.
### Export Formats
We also support a variety of model export formats for deployment in different environments. Here are the available formats: