From 232cc9bde356b59460ee29171bfcd2e106a3a27a Mon Sep 17 00:00:00 2001 From: Francesco Mattioli Date: Sat, 18 Jan 2025 18:28:30 +0100 Subject: [PATCH] Moved IMX under Deployment section (#18742) Co-authored-by: UltralyticsAssistant --- docs/en/integrations/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/en/integrations/index.md b/docs/en/integrations/index.md index 8ed822bd..721d8453 100644 --- a/docs/en/integrations/index.md +++ b/docs/en/integrations/index.md @@ -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: