zh/integrations/ncnn/

[giscus[bot]]

zh/integrations/ncnn/

通过导出NCNN 格式，为移动和嵌入式设备优化YOLO11 模型。在资源有限的环境中提高性能。

https://docs.ultralytics.com/zh/integrations/ncnn/

[xiao5676wen]

How can I export ncnn using the PT file I trained myself?

[glenn-jocher]

@xiao5676wen to export your trained YOLO11 .pt model to NCNN format, load your model using the YOLO class in Python and then call the export method with format="ncnn". Example:

from ultralytics import YOLO

model = YOLO("path_to_your_trained_model.pt")
model.export(format="ncnn")  # Creates the NCNN model

For additional details, refer to the NCNN export documentation.

[UltralyticsAssistant]

👋 Hello, thank you for your interest in Ultralytics 🚀! It's exciting to see discussions around optimizing YOLO models for mobile and embedded devices. The link you shared regarding NCNN integration is a great starting point for exploring this topic: https://docs.ultralytics.com/zh/integrations/ncnn/.

If you're encountering issues or have specific queries regarding the NCNN export or deployment, here are a few recommendations:

1. If this is a 🐛 Bug Report, please provide a minimum reproducible example so our team can better understand and debug any potential issues.

2. If this is an ❓ integration or optimization question, please provide as much context as possible, such as your model configuration, device specifications, and any logs or error messages.

For further learning and exploration, you may also find these resources helpful:

• Integration Examples: Comprehensive guides for integrating YOLO with various platforms and tools.
• NCNN Documentation: Specific details about NCNN and its benefits for resource-constrained environments.

Upgrade

Ensure you're using the latest Ultralytics package and dependencies by upgrading your environment with the following command:

pip install -U ultralytics

Environments

To experiment with YOLO in diverse, GPU-enabled environments, check out the following setups:

• Notebooks with free GPU:
• Cloud services like Google Cloud and AWS.
• Docker Image for efficient deployment and reproducibility .

Status

This badge indicates the status of Ultralytics CI tests, which ensure the reliability of YOLO models across various environments. A green badge means all tests are successful ✅.

Lastly, this is an automated response to guide you as quickly as possible, but rest assured that an Ultralytics engineer will follow up soon to assist you further. Feel free to share additional information or questions in the meantime 😊.

[xiao5676wen]

I have created a single target tracking model for deployment on Android devices. How can I export the ncnn file for deployment using my own model?

[xiao5676wen]

I have resolved the issue of using the model trained by ncnn in Python and am now preparing to deploy Java references for Android. Excuse me!

[glenn-jocher]

@xiao5676wen thank you for your update. To deploy your YOLO11 NCNN model on Android, you'll need to build NCNN for Android using Tencent NCNN's build guide and integrate the exported model using the NCNN Android SDK. For detailed integration steps, refer to the official NCNN documentation for Android. Let me know if you encounter issues!