Inference segmentation model for edge devices

[Sibi-s-5181]

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What is the best way with high inference speed for yolov5s-seg models, because I see tflite ,tensorRT inferences only for Bounding box models. Need a solution for better inference in devices like Raspberry pi for segmentation model

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[glenn-jocher]

@Sibi-s-5181 the best way to achieve high inference speed for YOLOv5s-seg models on edge devices like Raspberry Pi is to use the optimized TensorRT inference engine. TensorRT supports optimization and acceleration of various deep learning models, including YOLOv5 object detection models.

To use TensorRT for inference with segmentation models, you will need to convert your YOLOv5s-seg model to the ONNX format. Once you have the ONNX file, you can use the TensorRT Python API to optimize and deploy the model on your edge device.

There is currently no official support for tflite inference with YOLOv5 segmentation models, so the recommended approach is to use TensorRT for the best inference performance on edge devices.

Please note that the exact steps and optimizations may vary depending on your specific requirements and hardware capabilities. I recommend referring to the TensorRT documentation and examples for detailed instructions on how to optimize and deploy YOLOv5s-seg models using TensorRT.

If you encounter any specific issues or need further assistance during the process, feel free to ask for help, and our community or the Ultralytics team will be happy to assist you.

Thank you for your interest in YOLOv5, and I hope this information helps you achieve better inference performance on your Raspberry Pi device!

[Sibi-s-5181]

thanks for your response can you be more specific to the steps to inference Yolov5s-seg model using tensorRT also it would be better if you provide reference links

[glenn-jocher]

@Sibi-s-5181-5181, thanks for your interest in optimizing YOLOv5s-seg models with TensorRT!

To infer YOLOv5s-seg models using TensorRT, here are the general steps:

1. Convert the YOLOv5s-seg model to ONNX format: The first step is to export the model in the ONNX format using the --export ONNX flag when running the detect.py script in YOLOv5. This will generate an ONNX file for the segmentation model.

2. Install TensorRT: You need to have TensorRT installed on your system. Please refer to the NVIDIA TensorRT documentation for detailed installation instructions specific to your environment.

3. Optimize the model with TensorRT: Once you have the ONNX file, you can use the TensorRT Python API to optimize the model for your target platform. This involves creating a TensorRT engine from the ONNX file and applying optimizations like layer fusion, precision calibration, and dynamic tensor memory allocation. The exact steps will depend on your specific requirements and hardware configuration.

4. Deploy the optimized model: With the optimized TensorRT engine, you can now deploy the model on your target device and perform inference. You can use the TensorRT Python API to load and run the optimized engine, feeding the input images and retrieving the segmentation predictions.

As for reference links, you can find more detailed information and examples in the NVIDIA TensorRT documentation. It provides step-by-step instructions for optimizing models using TensorRT, along with code snippets and best practices.

I hope this helps you get started with optimizing YOLOv5s-seg models using TensorRT. If you have any further questions or need additional guidance, don't hesitate to ask. The YOLOv5 community and the Ultralytics team are here to support you along the way!

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