Code example
Image Classifier (ExecuTorch + XNNPACK)
An on-device evaluation harness for compatible ExecuTorch image classification models that use the XNNPACK backend and are hosted on Hugging Face.
Evaluation Conditions
Image Classification · Raspberry Pi 5 · INT8 · 224×224 · ImageNet-1k (val)
| Metric | Resultsvs baseline | Change | |
|---|---|---|---|
| End to end latency p50 | 3.63ms6.89ms | −3.26 ms | |
| End to end latency p90 | 3.78ms6.95ms | −3.17 ms | |
| End to end latency p99 | 5.27ms8.42ms | −3.14 ms | |
| Peak memory | 9.22MB16.53MB | −7.31 MB | |
| Average memory | 8.70MB16.18MB | −7.48 MB | |
| Time to first inference | 7.52ms10.78ms | −3.26 ms | |
| Frames per second | 275.39fps145.22fps | +130.17 fps | |
| Accuracy - Top 1 | 68.76%69.36% | −0.60% | |
| Accuracy - Top 5 | 88.01%88.31% | −0.30% |
The Optimized Model is provided as a reference implementation to demonstrate and evaluate execution and performance on Arm-based systems. It is not a production-ready or supported solution.
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| Configuration | Baseline | Arm Optimized |
|---|---|---|
| Dataset | ImageNet-1k (val) | ImageNet-1k (val) |
| Sample count | 50,000 | 50,000 |
| Batch size | 1 | 1 |
| Input resolution | 224x224 | 224x224 |
| Runs | 100 | 100 |
| Warmup runs | 10 | 10 |