Principle:Roboflow Rf detr ONNX Model Export

Knowledge Sources	ONNX Spec PyTorch ONNX RF-DETR
Domains	Deployment, Model_Export
Last Updated	2026-02-08 15:00 GMT

Overview

The process of converting a trained PyTorch detection model into the ONNX interchange format for cross-platform deployment.

Description

ONNX export converts the PyTorch model graph into a static, framework-agnostic representation. The export process:

1. Prepares the model: Switches to eval mode and calls model.export() to set the ONNX-compatible forward path
2. Traces the model: Uses torch.onnx.export with a sample input to trace all operations
3. Produces ONNX file: Saves the graph with inputs, outputs, and weights

The resulting ONNX model can be deployed via ONNX Runtime, TensorRT, or other ONNX-compatible runtimes.

Usage

Use this principle after training to prepare a model for production deployment. ONNX export is particularly useful for deploying to environments where PyTorch is not available or when TensorRT optimization is desired.

Theoretical Basis

ONNX export works via tracing: recording all PyTorch operations executed during a forward pass with a sample input. This captures the static computation graph. Key considerations:

• The model must produce deterministic output paths (no data-dependent control flow)
• The opset version determines which ONNX operators are available (RF-DETR uses opset 17)
• Constant folding during export optimizes static computations

Related Pages

Implemented By

• Implementation:Roboflow_Rf_detr_RFDETR_Export

Uses Heuristic

• Heuristic:Roboflow_Rf_detr_Resolution_Divisibility_Rule