Principle:Roboflow Rf detr ONNX Model Simplification
| Knowledge Sources | |
|---|---|
| Domains | Deployment, Model_Optimization |
| Last Updated | 2026-02-08 15:00 GMT |
Overview
The process of optimizing an ONNX model graph through constant folding, dead code elimination, and operator fusion to reduce model size and improve inference speed.
Description
ONNX simplification applies graph optimization passes to remove redundant operations from the exported model. This includes:
- Constant folding: Pre-computing operations with constant inputs
- Dead code elimination: Removing unused graph nodes
- Operator fusion: Combining sequential operations into single optimized operations
- Shape inference: Propagating tensor shapes through the graph for runtime optimization
These optimizations typically reduce model size and improve inference latency without affecting numerical accuracy.
Usage
Apply simplification after ONNX export and before deployment. This is particularly important when targeting TensorRT or other optimizing runtimes.
Theoretical Basis
Graph optimization transforms the ONNX computation graph while preserving semantic equivalence. The key passes are:
- Constant propagation: Evaluate subgraphs with known inputs at optimization time
- Graph restructuring: Merge compatible operations (e.g., Conv + BatchNorm fusion)
- Validation: Verify numerical equivalence between original and simplified models
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