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 | 25.33ms90.77ms | −65.44 ms | |
| End to end latency p90 | 25.38ms90.96ms | −65.58 ms | |
| End to end latency p99 | 25.43ms91.74ms | −66.31 ms | |
| Peak memory | 18.12MB41.97MB | −23.85 MB | |
| Average memory | 18.12MB39.34MB | −21.22 MB | |
| Time to first inference | 27.70ms122.15ms | −94.45 ms | |
| Frames per second | 39.49fps11.02fps | +28.47 fps | |
| Accuracy - Top 1 | 69.71%69.77% | −0.06% | |
| Accuracy - Top 5 | 89.52%89.53% | −0.01% |
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 |