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Data checked 2026-08-13. Data may be stale - beyond the review cycle. Review cycles are documented on the Methodology page. Methodology

wang-xinyu/tensorrtx

Software Tool Tier A edge_ai MIT
Official confirmed

Implementation of popular deep learning networks with TensorRT network definition API

Engineering Snapshot

Use cases & tasks 4
Best suited for
  • Edge AI inference on NVIDIA GPUs (Jetson, discrete)
  • Learning TensorRT network definition API by example
Primary tasks
  • Deploying YOLO11, DETR, ResNet, MobileNetV2/V3, Swin-Transformer on TensorRT
  • Porting PyTorch models to TensorRT via reference implementations
Stack & ecosystem
Resource type
Software Tool
Ecosystem
arcface · crnn · detr · mnasnet · mobilenetv2 · mobilenetv3
License & compliance
License
MIT (Inferred)
Lifecycle & freshness Show
Maintenance
Active
Latest version
Not recorded
Last activity
2026-08-13
Last checked
2026-08-13
Verification
Official confirmed

What It Solves

Engineers deploying deep learning models on NVIDIA GPUs need low-latency inference; TensorRT requires manual network definition. tensorrtx provides reference implementations for popular architectures using TensorRT's C++ API.

Primary use cases

  • Deploying YOLO11, DETR, ResNet, MobileNetV2/V3, Swin-Transformer on TensorRT
  • Porting PyTorch models to TensorRT via reference implementations

Secondary use cases

  • Benchmarking TensorRT vs ONNX Runtime/OpenVINO
  • Developing custom TensorRT plugins

When to Use

Consider when

  • Target hardware is NVIDIA GPU
  • Maximum inference throughput required
  • Model architecture is in supported list (YOLO11, DETR, ResNet, MobileNet, Swin, etc.)

Verify before adopting

  • TensorRT version compatibility with your CUDA/cuDNN
  • Numerical accuracy after FP16/INT8 optimization
  • Maintenance status of specific model implementation

Start Here

repo https://github.com/wang-xinyu/tensorrtx

Adoption Checklist

  • Needs verification TensorRT version compatibility with your CUDA/cuDNN
  • Needs verification Numerical accuracy after FP16/INT8 optimization
  • Needs verification Maintenance status of specific model implementation

Each check stays "needs verification" until an official source confirms it; unconfirmed items are never marked verified.

Known Limitations & Unknowns

Known limitations

  • NVIDIA GPU only
  • Manual network definition (no automatic ONNX parser)
  • Limited to architectures implemented in repo
  • INT8 calibration not included for all models

Not publicly verified

  • Performance benchmarks on specific Jetson modules
  • Support for TensorRT 10.x features
  • Dynamic input shape handling

Alternatives & Related Tools

Related tools

How is it used?

Start from the recorded entry points below, then validate against the technical checklist.

Technical checklist

  • OK License identified Recorded: MIT
  • OK Maintenance signal Active
  • OK Verification status Official confirmed
  • OK Source evidence attached 1 source record(s)
  • NEEDS REVIEW Latest version recorded Not recorded

Official Links

Metadata & Governance

License MIT — Inferred
Commercial modelUnknown
Maintenance statusActive
Verification status Official confirmed — Confirmed via the official repository API responses in SourceRefs below.
Latest versionNot recorded
Latest releaseNot recorded
Last activity2026-08-13
Last checked2026-08-13
First seenNot recorded

Dataset facts

Facts above come from the official dataset card only; unconfirmed fields stay unknown.

Related Resources & Dependencies

  • tensorrt — built on (confirmed)
  • detr — implements (confirmed)

Recent Activity

Related Knowledge

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