jkjung-avt/tensorrt_demos

Health v2 · maintenance only

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1792 stars · 543 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Python demo programs showing how to optimize and run deep learning models (YOLOv4, YOLOv3, SSD, MTCNN, GoogLeNet, MODNet) with NVIDIA TensorRT. It targets NVIDIA Jetson developer kits and x86_64 PCs with NVIDIA GPUs, providing optimized inference examples with benchmarked performance.

Use cases

• run yolov4 object detection on jetson nano with tensorrt
• optimize deep learning models with tensorrt for jetson
• run face detection mtcnn on jetson xavier nx
• video matting with modnet on jetson
• benchmark ssd mobilenet inference speed on jetson
• run tensorrt inference on x86 gpu
• convert darknet or caffe models to tensorrt engines

When to choose

• you need fast optimized inference of these specific models on NVIDIA Jetson hardware
• you want reference code for converting Caffe/TensorFlow/DarkNet/PyTorch models to TensorRT
• you are benchmarking object detection performance on embedded NVIDIA devices

When to avoid

• you need a production-ready inference framework rather than demo examples
• you target non-NVIDIA hardware or CPUs
• you need models not covered by the demos (e.g., transformers or segmentation models)
• you want training code rather than inference

Facets

library · maturity maintenance

machine-learning image-processing computer-vision gpu-computing developer-tools deep-learning computer-vision gpu-computing embedded-systems developer-tools python embedded tensorrt jetson yolov4 yolov3 ssd-mobilenet mtcnn googlenet modnet object-detection model-optimization inference linux gpu

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