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TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet
| Date | Stars |
|---|---|
| 2026-07-24 | 1792 |
| 2026-07-25 | 1792 |
| 2026-07-28 | 1792 |
| 2026-07-30 | 1792 |
| 2026-08-06 | 1792 |
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# tensorrt_demos
This repo is a collection of examples demonstrating how to optimize Caffe/TensorFlow/DarkNet/PyTorch models with TensorRT.
Highlights:
* Run an optimized "MODNet" video matting model at ~21 FPS on Jetson Xavier NX.
* Run an optimized "yolov4-416" object detector at ~4.6 FPS on Jetson Nano.
* Run an optimized "yolov3-416" object detector at ~4.9 FPS on Jetson Nano.
* Run an optimized "ssd_mobilenet_v1_coco" object detector ("trt_ssd_async.py") at 27~28 FPS on Jetson Nano.
* Run an optimized "MTCNN" face detector at 6~11 FPS on Jetson Nano.
* Run an optimized "GoogLeNet" image classifier at "~16 ms per image (inference only)" on Jetson Nano.
Supported hardware:
* NVIDIA Jetson
- All NVIDIA Jetson Developer Kits, e.g. [Jetson AGX Orin DevKit](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/#advanced-features), [Jetson AGX Xavier DevKit](https://developer.nvidia.com/embedded/jetson-agx-xavier-developer-kit), [Jetson Xavier NX DevKit](https://developer.nvidia.com/embedded/jetson-xavier-nx-devkit), Jetson TX2 DevKit, [Jetson Nano DevKit](https://developer.nvidia.com/embedded/jetson-nano-developer-kit).
* x86_64 PC with modern NVIDIA GPU(s). Refer to [README_x86.md](https://github.com/jkjung-avt/tensorrt_demos/blob/master/README_x86.md) for more information.
Table of contents
-----------------
* [Prerequisite](#prerequisite)
* [Demo #1: GoogLeNet](#googlenet)
* [Demo #2: MTCNN](#mtcnn)
* [Demo #3: SSD](#ssd)
* [Demo #4: YOLOv3](#yolov3)
* [Demo #5: YOLOv4](#yolov4)
* [Demo #6: Using INT8 and DLA core](#int8_and_dla)
* [Demo #7: MODNet](#modnet)
<a name="prerequisite"></a>
Prerequisite
------------
The code in this repository was tested on Jetson Nano, TX2, and Xavier NX DevKits. In order to run the demos below, first make sure you have the proper version of image (JetPack) installed on the target Jetson system. For example, [Setting up Jetson Nano: The Basics](https://jkjung-avt.github.io/setting-up-nano/) and [Setting up Jetson Xavier NX](https://jkjung-avt.github.io/setting-up-xavier-nx/).
More specifically, the target Jetson system must have TensorRT libraries installed.
* Demo #1 and Demo #2: works for TensorRT 3.x+,
* Demo #3: requires TensoRT 5.x+,
* Demo #4 and Demo #5: requires TensorRT 6.x+.
* Demo #6 part 1: INT8 requires TensorRT 6.x+ and only works on GPUs with CUDA compute 6.1+.
* Demo #6 part 2: DLA core requires TensorRT 7.x+ (is only tested on Jetson Xavier NX).
* Demo #7: requires TensorRT 7.x+.
You could check which version of TensorRT has been installed on your Jetson system by looking at file names of the libraries. For example, TensorRT v5.1.6 (JetPack-4.2.2) was present on one of my Jetson Nano DevKits.
```shell
$ ls /usr/lib/aarch64-linux-gnu/libnvinfer.so*
/usr/lib/aarch64-linux-gnu/libnvinfer.so
/usr/lib/aarch64-linux-gnu/libnvinfer.so.5
/usr/lib/aarch64-linux-gnu/libnvinfer.so.5.1.6
```
Furthermore, all demo programs in this repository require "cv2" (OpenCV) module for python3. You could use the "cv2" module which came in the JetPack. Or, if you'd prefer building your own, refer to [Installing OpenCV 3.4.6 on Jetson Nano](https://jkjung-avt.github.io/opencv-on-nano/) for how to build from source and install opencv-3.4.6 on your Jetson system.
If you plan to run Demo #3 (SSD), you'd also need to have "tensorflow-1.x" installed. You could probably use the [official tensorflow wheels provided by NVIDIA](https://docs.nvidia.com/deeplearning/frameworks/pdf/Install-TensorFlow-Jetson-Platform.pdf), or refer to [Building TensorFlow 1.12.2 on Jetson Nano](https://jkjung-avt.github.io/build-tensorflow-1.12.2/) for how to install tensorflow-1.12.2 on the Jetson system.
Or if you plan to run Demo #4 and Demo #5, you'd need to have "protobuf" installed. I recommend installing "protobuf-3.8.0" using my [install_protobuf-3.8.0.sh](https://github.com/jkjung-avt/jetson_nano/blob/master/install_protobuf-3.8.0.sh) script. This script would take a coupleExcerpt of 34,823 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
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