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.

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

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:

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

Model

• YOLOv3: An Incremental Improvement

Dataset

• COCO dataset

Documentation

• YOLOv3-608 paper
• Introduction To NVIDIA’s TensorRT Samples
• Working With TensorRT Using The Python API
• NVIDIA’s TensorRT Documentation Library