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docs/en/integrations/ncnn.md
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@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, NCNN, model export, machine learning, deployment,
# How to Export to NCNN from YOLOv8 for Smooth Deployment
Deploying computer vision models on devices with limited computational power, such as mobile or embedded systems, can be tricky. You need to make sure you use a format optimized for optimal performance. This makes sure that even devices with limited processing power can handle advanced computer vision tasks well.
Deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models on devices with limited computational power, such as mobile or embedded systems, can be tricky. You need to make sure you use a format optimized for optimal performance. This makes sure that even devices with limited processing power can handle advanced computer vision tasks well.
The export to NCNN format feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for lightweight device-based applications. In this guide, we'll walk you through how to convert your models to the NCNN format, making it easier for your models to perform well on various mobile and embedded devices.
@ -16,17 +16,17 @@ The export to NCNN format feature allows you to optimize your [Ultralytics YOLOv
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ncnn-overview.avif" alt="NCNN overview">
</p>
The [NCNN](https://github.com/Tencent/ncnn) framework, developed by Tencent, is a high-performance neural network inference computing framework optimized specifically for mobile platforms, including mobile phones, embedded devices, and IoT devices. NCNN is compatible with a wide range of platforms, including Linux, Android, iOS, and macOS.
The [NCNN](https://github.com/Tencent/ncnn) framework, developed by Tencent, is a high-performance [neural network](https://www.ultralytics.com/glossary/neural-network-nn) inference computing framework optimized specifically for mobile platforms, including mobile phones, embedded devices, and IoT devices. NCNN is compatible with a wide range of platforms, including Linux, Android, iOS, and macOS.
NCNN is known for its fast processing speed on mobile CPUs and enables rapid deployment of deep learning models to mobile platforms. This makes it easier to build smart apps, putting the power of AI right at your fingertips.
NCNN is known for its fast processing speed on mobile CPUs and enables rapid deployment of [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models to mobile platforms. This makes it easier to build smart apps, putting the power of AI right at your fingertips.
## Key Features of NCNN Models
NCNN models offer a wide range of key features that enable on-device machine learning by helping developers run their models on mobile, embedded, and edge devices:
NCNN models offer a wide range of key features that enable on-device [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) by helping developers run their models on mobile, embedded, and edge devices:
- **Efficient and High-Performance**: NCNN models are made to be efficient and lightweight, optimized for running on mobile and embedded devices like Raspberry Pi with limited resources. They can also achieve high performance with high accuracy on various computer vision-based tasks.
- **Efficient and High-Performance**: NCNN models are made to be efficient and lightweight, optimized for running on mobile and embedded devices like Raspberry Pi with limited resources. They can also achieve high performance with high [accuracy](https://www.ultralytics.com/glossary/accuracy) on various computer vision-based tasks.
- **Quantization**: NCNN models often support quantization which is a technique that reduces the precision of the model's weights and activations. This leads to further improvements in performance and reduces memory footprint.
- **Quantization**: NCNN models often support quantization which is a technique that reduces the [precision](https://www.ultralytics.com/glossary/precision) of the model's weights and activations. This leads to further improvements in performance and reduces memory footprint.
- **Compatibility**: NCNN models are compatible with popular deep learning frameworks like [TensorFlow](https://www.tensorflow.org/), [Caffe](https://caffe.berkeleyvision.org/), and [ONNX](https://onnx.ai/). This compatibility allows developers to use existing models and workflows easily.
@ -103,7 +103,7 @@ For more details about supported export options, visit the [Ultralytics document
After successfully exporting your Ultralytics YOLOv8 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:
- **[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 through Android applications.
- **[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.
- **[macOS](https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-macos)**: Understand how to use NCNN models for performing tasks through macOS.
@ -159,12 +159,12 @@ For more details, see the [Export to NCNN](#why-should-you-export-to-ncnn) secti
NCNN, developed by Tencent, is specifically optimized for mobile platforms. Key reasons to use NCNN include:
- **High Performance**: Designed for efficient and fast processing on mobile CPUs.
- **Cross-Platform**: Compatible with popular frameworks such as TensorFlow and ONNX, making it easier to convert and deploy models across different platforms.
- **Cross-Platform**: Compatible with popular frameworks such as [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and ONNX, making it easier to convert and deploy models across different platforms.
- **Community Support**: Active community support ensures continual improvements and updates.
To understand more, visit the [NCNN overview](#key-features-of-ncnn-models) in the documentation.
### What platforms are supported for NCNN model deployment?
### What platforms are supported for NCNN [model deployment](https://www.ultralytics.com/glossary/model-deployment)?
NCNN is versatile and supports various platforms: