YOLOv5 Model Optimization

[Waqas649]

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Hello, I trained a custom yolov5 model, now I want to deploy it, but first I want to optimize it, is there any easy way to optimize it with openvino, some months ago, I used a online toolkit to optimize it but now its not found, anyone can help pls. Thanks

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[github-actions[bot]]

👋 Hello @Waqas649, thank you for raising an issue about Ultralytics HUB 🚀! Please visit our HUB Docs to learn more:

• Quickstart. Start training and deploying YOLO models with HUB in seconds.
• Datasets: Preparing and Uploading. Learn how to prepare and upload your datasets to HUB in YOLO format.
• Projects: Creating and Managing. Group your models into projects for improved organization.
• Models: Training and Exporting. Train YOLOv5 and YOLOv8 models on your custom datasets and export them to various formats for deployment.
• Integrations. Explore different integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle.
• Ultralytics HUB App. Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
  • iOS. Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
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• Inference API. Understand how to use the Inference API for running your trained models in the cloud to generate predictions.

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[pderrenger]

Hello! 😊 Great to hear about your progress with your custom YOLOv5 model.

Optimizing your model for deployment with OpenVINO can indeed streamline the inference process. While we don't have a predefined online toolkit, converting your model to an OpenVINO-compatible format involves a couple of steps, starting with exporting your model to ONNX format. After that, you can utilize the OpenVINO Model Optimizer to convert the ONNX model to an IR (Intermediate Representation) format that's optimized for inference on various Intel hardware.

For a detailed step-by-step guide, including necessary commands and further optimization tips, please refer to the "Deployment" section in our official documentation at https://docs.ultralytics.com/hub. It offers a comprehensive walkthrough that should fit your needs.

If you encounter any specific issues or have further questions during the process, feel free to reach out here again. Happy optimizing! 🚀