Principle:Roboflow Rf detr ONNX Runtime Validation
| Knowledge Sources | |
|---|---|
| Domains | Deployment, Inference |
| Last Updated | 2026-02-08 15:00 GMT |
Overview
The process of validating and running inference on an exported ONNX model using ONNX Runtime.
Description
After exporting a model to ONNX format, validation ensures the exported model produces correct predictions. ONNX Runtime provides an optimized inference engine that can run the ONNX model on CPU or GPU without requiring PyTorch. This step verifies model correctness and provides a deployment-ready inference path.
Usage
Use this principle to verify ONNX export correctness and to run inference in production environments where PyTorch is not available or where ONNX Runtime's optimizations provide better performance.
Theoretical Basis
ONNX Runtime applies runtime optimizations including:
- Graph optimization: Operator fusion, memory planning, and parallelism
- Execution providers: Hardware-specific backends (CUDA, TensorRT, DirectML, OpenVINO)
- Memory management: Optimized memory allocation and reuse patterns
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