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README.md
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---
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license: apache-2.0
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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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- object-detection
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---
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# yolox_s — ExecuTorch XNNPACK
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`yolox_s_xnnpack_fp32.pte` (35.9 MB, fp32, XNNPACK-delegated)
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- **Source**: Megvii-BaseDetection/YOLOX (yolox_s)
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- **License**: Apache-2.0
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- **Input**: [[1, 3, 640, 640]] — BGR 0..255 float, NO normalization (YOLOX v0.3+ convention), 640x640 letterbox pad 114
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- **Output**: [1,8400,85]: cx,cy,w,h (input px), objectness, 80 class scores; postprocess = obj*cls threshold + NMS (required)
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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, 8400, 85] | 3.021e-03 | 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 24.7 ms vs torch eager 38.8 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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**Notes**: max_abs_diff ~3e-3 is on decoded pixel-coordinate outputs (values up to 640), i.e. relative error <1e-5; corr 1.000000.
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