wang-xinyu/tensorrtx
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
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.
- Official site https://github.com/wang-xinyu/tensorrtx
- Repository https://github.com/wang-xinyu/tensorrtx
- Documentation https://github.com/wang-xinyu/tensorrtx
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 model | Unknown |
| Maintenance status | Active |
| Verification status | Official confirmed — Confirmed via the official repository API responses in SourceRefs below. |
| Latest version | Not recorded |
| Latest release | Not recorded |
| Last activity | 2026-08-13 |
| Last checked | 2026-08-13 |
| First seen | Not recorded |
Dataset facts
Facts above come from the official dataset card only; unconfirmed fields stay unknown.
Related Resources & Dependencies
Recent Activity
New resource: wang-xinyu/tensorrtx
New repository resource added by the sprint promote pipeline.
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