[DEPRECATED] Object Detection With The ONNX TensorRT Backend In Python

This sample has been deprecated starting from TensorRT 10.13 due to unstable yolov3 weights. See also pjreddie/darknet#2655

Table Of Contents

Description
How does this sample work?
Prerequisites
Running the sample
Additional resources
License
Changelog
Known issues

Description

This sample, yolov3_onnx, implements a full ONNX-based pipeline for performing inference with the YOLOv3 network, with an input size of 608 x 608 pixels, including pre and post-processing. This sample is based on the YOLOv3-608 paper.

How does this sample work?

First, the original YOLOv3 specification from the paper is converted to the Open Neural Network Exchange (ONNX) format in yolov3_to_onnx.py (only has to be done once).

Second, this ONNX representation of YOLOv3 is used to build a TensorRT engine, followed by inference on a sample image in onnx_to_tensorrt.py. The predicted bounding boxes are finally drawn to the original input image and saved to disk.

After inference, post-processing including bounding-box clustering is applied. The resulting bounding boxes are eventually drawn to a new image file and stored on disk for inspection.

Note: This sample is not supported on Ubuntu 14.04 and older.

Prerequisites

For specific software versions, see the TensorRT Installation Guide.

Install the dependencies for Python.
```
pip3 install -r requirements.txt
```
Download sample data. See the "Download Sample Data" section of the general setup guide.

Running the sample

The data directory needs to be specified (either via -d /path/to/data or environment varaiable TRT_DATA_DIR) when running these scripts. An error will be thrown if not. Taking TRT_DATA_DIR approach in following example.

Create an ONNX version of YOLOv3 with the following command.

python3 yolov3_to_onnx.py

When running the above command for the first time, the output should look similar to the following:

[...]
%106_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]]
(%105_convolutional_lrelu, %106_convolutional_conv_weights, %106_convolutional_conv_bias)
return %082_convolutional, %094_convolutional,%106_convolutional
}

Build a TensorRT engine from the generated ONNX file and run inference on a sample image

python3 onnx_to_tensorrt.py

When running the above command for the first time, the output should look similar to the following:

Building an engine from file yolov3.onnx, this may take a while...
Running inference on image dog.jpg...
Saved image with bounding boxes of detected objects to dog_bboxes.jpg.

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

Loading ONNX file from path yolov3.onnx...
Beginning ONNX file parsing
Completed parsing of ONNX file
Building an engine from file yolov3.onnx; this may take a while...
Completed creating Engine
Running inference on image dog.jpg...
[[135.14841333 219.59879284 184.30209195 324.0265199 ]
  [ 98.30805074 135.72613533 499.71263299 299.25579652]
  [478.00605802 81.25702449 210.57787895 86.91502688]] [0.99854713 0.99880403 0.93829258] [16 1 7]
Saved image with bounding boxes of detected objects to dog_bboxes.png.

You should be able to visually confirm whether the detection was correct.

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

YOLOv3: An Incremental Improvement

Dataset

COCO dataset

Documentation

License

For terms and conditions for use, reproduction, and distribution, see the TensorRT Software License Agreement documentation.

Changelog

August 2025

Removed support for Python versions < 3.10.

August 2023

Removed support for Python versions < 3.8.
This sample was updated to support protobuf 3.20.3 for Python>=3.8
Update ONNX version support to 1.14.0

March 2019

This README.md file was recreated, updated and reviewed.

Known issues

When installing the requirements with Python 3.10, there is a known issue for building onnx. The recommendation is to use a python version < 3.10 when running the sample.