Principle:Roboflow Rf detr Deployment Benchmarking
| Knowledge Sources | |
|---|---|
| Domains | Deployment, Benchmarking, Performance_Evaluation |
| Last Updated | 2026-02-08 15:00 GMT |
Overview
Systematic process for measuring inference latency and detection accuracy of exported models in their target deployment runtime (ONNX Runtime or TensorRT).
Description
Deployment Benchmarking is the practice of evaluating exported model artifacts in the exact runtime they will use in production. Unlike development-time profiling (which measures the PyTorch model), deployment benchmarking operates on the optimized export format (ONNX or TensorRT engine) and measures end-to-end latency including preprocessing, model inference, and post-processing. This process validates two critical properties: (1) that accuracy is preserved after export by comparing mAP against the COCO validation set, and (2) that latency meets real-time requirements by measuring per-image inference time with proper GPU synchronization. Reliable latency measurement requires multiple repetitions per image to account for GPU warm-up and scheduling variance.
Usage
Apply this principle after exporting a detection model to an optimized format and before production deployment. It serves as the final validation gate, ensuring the export process has not degraded accuracy and that the optimized model meets the target latency budget. It is especially critical when comparing ONNX Runtime vs TensorRT backends or when evaluating FP16 vs FP32 precision modes.
Theoretical Basis
Deployment benchmarking follows a standard evaluation protocol:
1. Preprocessing Consistency: The exported model must receive inputs preprocessed identically to training (same resize strategy, normalization constants, color space). Deviations here are a common source of accuracy loss post-export.
2. Latency Measurement:
# Abstract algorithm for reliable latency measurement
gpu_sync() # Ensure GPU is idle
start = wall_clock()
for repeat in range(N):
output = runtime.infer(input)
gpu_sync() # Wait for all GPU work to complete
avg_latency = (wall_clock() - start) / N
GPU synchronization before and after is essential because GPU operations are asynchronous. Without it, measured latency reflects only kernel launch overhead, not actual computation time.
3. Accuracy Validation: Standard COCO evaluation (mAP@[0.5:0.95]) is computed on the full validation set to detect any accuracy regression from quantization, graph optimization, or operator approximation.