Code example
Image Classification Example Application
Fine-tune and quantize DeiT-Tiny on the Oxford-IIIT Pet dataset, export it with ExecuTorch, and run image classification on a Corstone-320 Ethos-U85 FVP.
Fine-tune and quantize DeiT-Tiny on the Oxford-IIIT Pet dataset, export it with ExecuTorch, and run image classification on a Corstone-320 Ethos-U85 FVP.
Classify objects in images on an Ethos-U NPU using MobileNet V2 with TensorFlow Lite Micro or MobileNet V2 and DeiT-Tiny with ExecuTorch.
Evaluation Conditions
Image Classification · Alif DK-E8 · INT8 · 224×224 · ImageNet-1k (val)
| Metric | Resultsvs baseline | Change | |
|---|---|---|---|
| End to end latency p50 | 81.59ms-ms | ||
| End to end latency p90 | 81.59ms |
| Peak memory | 2.56MB-MB |
| Frames per second | 12.26fps-fps |
| Accuracy - Top 1 | 67.95%72.13% | −4.18% |
| Accuracy - Top 5 | 88.47%91.13% | −2.66% |
| Configuration | Baseline | Arm Optimized |
|---|---|---|
| Dataset | ImageNet-1k (val) | ImageNet-1k (val) |
| Sample count | 50,000 | 50,000 |
| Batch size | 1 | 1 |
| Input resolution |
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| 224x224 |
224x224 |
| Runs | 1 | 100 |
| Warmup runs | N/A | 10 |