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README.md
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---
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license: bsd-3-clause
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tags:
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- executorch
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- xnnpack
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- pte
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- on-device
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- image-classification
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---
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# efficientnet_b1 — ExecuTorch XNNPACK
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`efficientnet_b1_xnnpack_fp32.pte` (31.2 MB, fp32, XNNPACK-delegated)
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- **Source**: torchvision efficientnet_b1 IMAGENET1K_V2
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- **License**: BSD-3-Clause
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- **Input**: [[1, 3, 240, 240]] — RGB, ImageNet norm, 240x240
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- **Output**: ImageNet logits [1,1000]
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## Verification (Mac arm64, executorch 1.4.0, torch 2.13.0)
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Parity vs torch fp32 eager on random input:
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| output | shape | max_abs_diff | corr |
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|--------|-------|--------------|------|
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| 0 | [1, 1000] | 6.676e-06 | 1.000000 |
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Median latency over 10 runs (single Mac process, reference only — device numbers to follow):
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ExecuTorch 9.2 ms vs torch eager 345.4 ms.
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## Conversion
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torch.export -> to_edge_transform_and_lower(XnnpackPartitioner) -> .pte
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(conversion scripts: `convert/` in the conversion repo)
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