wang-xinyu/tensorrtx

Health v2 · maintenance only

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7827 stars · 1858 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A C++ collection of popular deep learning networks (YOLO variants, ResNet, MobileNet, Swin Transformer, OCR models, and more) implemented from scratch with the NVIDIA TensorRT network definition API. It exports trained weights to .wts files and builds optimized TensorRT inference engines without relying on ONNX or other parsers.

Use cases

• deploy yolo models with tensorrt in c++
• build tensorrt engines without onnx parser
• run deep learning inference on nvidia gpu with maximum performance
• convert pytorch weights to tensorrt
• learn how popular networks are structured layer by layer
• integrate preprocessing and postprocessing into a tensorrt network
• deploy vision transformer or swin transformer on jetson

When to choose

• you need fine-grained control over TensorRT network layers or want to modify/merge layers
• ONNX or UFF parsers fail or produce suboptimal engines for your model
• you want to learn network architectures by building them explicitly
• you need a reference C++ implementation of YOLO or other vision models on TensorRT

When to avoid

• your model exports cleanly to ONNX and trtexec or onnx-tensorrt works fine
• you want a high-level Python deployment pipeline with minimal code
• you need training or fine-tuning - this is inference-only
• you target non-NVIDIA hardware

Facets

library · maturity active

deep-learning machine-learning computer-vision image-processing ocr llm-inference deep-learning computer-vision machine-learning gpu-computing cpp windows cross-platform tensorrt nvidia inference yolo model-deployment network-definition-api wts-weights engine-building gpu linux

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