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This repository provides a sample to run yolov3 on int8 mode in tensorRT

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rui-shen-afk/yolov3_tensorRT_int8_calibration

 
 

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Description

This code implements a full ONNX-based pipeline for performing inference with the YOLOv3 network, using int8 calibration. This code is partly based on the offical sample "yolov3_onnx.py" given by Tensor RT. This sample is based on the YOLOv3-608 paper.

Note: This sample is not supported on Ubuntu 14.04 and older. Additionally, the yolov3_to_onnx.py script does not support Python 3.

Prerequisites

For specific software versions, see the TensorRT Installation Guide.

  1. Install ONNX-TensorRT: TensorRT backend for ONNX. ONNX-TensorRT includes layer implementations for the required ONNX operators Upsample and LeakyReLU.

  2. Install the dependencies for Python.

    • For Python 2 users, from the root directory, run: python2 -m pip install -r requirements.txt

    • For Python 3 users, from the root directory, run: python3 -m pip install -r requirements.txt

Running the sample

  1. Create an ONNX version of YOLOv3 with the following command. The Python script will also download all necessary files from the official mirrors (only once). python yolov3_to_onnx.py

    When running this sample you could get middle layer output by changing the definition of variable "output_layer_name", layer name can be get in yolov3.onnx

  2. Build a TensorRT engine from the generated ONNX file and run inference on a sample image, which will also be downloaded during the first run. python onnx_to_tensorrt_int8.py

    When running this sample you could get middle layer output by changing the definition of variable "output_layer_name", layer name can be found in yolov3.onnx

  3. Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:

Additional resources

The following resources provide a deeper understanding about the model used in this sample, as well as the dataset it was trained on:

Model

Dataset

Documentation

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This repository provides a sample to run yolov3 on int8 mode in tensorRT

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