feat: plateDetection/yolox-tiny-plate (open-lpr YOLOX-tiny, Apache-2.0)

open-lpr plate_yolox_tiny_640.onnx (faisalthaheem/open-lpr-models @ aed0df35, tag detector-2026.10.1) with the CamStack input/output contract baked into the graph; ONNX fp32, OpenVINO IR fp16, CoreML fp16. Built by scripts/build-yolox-plate-model.py (D718). Upstream LICENSE and NOTICE included verbatim.

plateDetection/yolox-tiny-plate/LICENSE

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plateDetection/yolox-tiny-plate/NOTICE

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+open-lpr models
+Copyright the open-lpr contributors
+
+This product includes software developed by third parties. The artifacts in
+this repository are NOT all ours; per-artifact provenance is recorded in
+manifest.json and summarised below.
+
+--------------------------------------------------------------------------------
+plate_yolox_tiny_640.onnx  and  best_ckpt.pth
+--------------------------------------------------------------------------------
+Produced by this project. Architecture from YOLOX (Megvii-BaseDetection/YOLOX),
+which is licensed Apache-2.0 for both its code and its same-repository release
+weights. Pretrained from COCO (yolox_tiny.pth), then fine-tuned on an annotated
+licence-plate corpus plus community-contributed images pseudo-labelled by a
+teacher model.
+
+The training imagery is not published. It consists of user-contributed
+photographs containing real licence plates, faces and vehicles, uploaded without
+consent obtained. Publishing it would create a searchable index of real vehicle
+registration numbers.
+
+Ultralytics YOLO weights are licensed AGPL-3.0 and are deliberately NOT used,
+redistributed, or derived from anywhere in this project.
+
+--------------------------------------------------------------------------------
+plate_ocr_ppocrv5_mobile.onnx  and  plate_ocr_dict.json
+--------------------------------------------------------------------------------
+NOT produced by this project. These are conversions of an upstream recognition
+model from PaddleOCR (https://github.com/PaddlePaddle/PaddleOCR), which is
+licensed under Apache-2.0.
+
+The specific upstream release was never recorded and cannot be recovered from
+the artifacts: the ONNX graph carries no producer, producer_version or
+doc_string, and no PaddlePaddle installation, wheel or shell history survives on
+the build host. The Apache-2.0 licence is therefore asserted at project level
+and is NOT pinned to a verified release.
+
+These two files are a MATCHED PAIR and must be used together. Decoding assumes
+len(dictionary) + 2 output classes; substituting either without the other
+produces silently incorrect text rather than an error.

plateDetection/yolox-tiny-plate/README.md

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+---
+license: apache-2.0
+library_name: onnx
+tags:
+  - object-detection
+  - license-plate-detection
+  - yolox
+  - camstack
+---
+
+# YOLOX-tiny licence-plate detector — CamStack build
+
+Single-class (`plate`) detector, re-packaged for the CamStack inference pool.
+The `plate-detection` step's only model, catalog id `yolox-tiny-plate`.
+
+| Format | File | Precision |
+| --- | --- | --- |
+| ONNX | `onnx/camstack-yolox-tiny-plate.onnx` | fp32 |
+| OpenVINO IR | `openvino/camstack-yolox-tiny-plate.{xml,bin}` | fp16 weights |
+| CoreML | `coreml/camstack-yolox-tiny-plate.mlpackage` | fp16 |
+
+No TFLite / Edge TPU build exists: YOLOX's Focus stem does not compile for the
+Edge TPU, and a Coral node runs this step on another device.
+
+## Source
+
+- Model: `plate_yolox_tiny_640.onnx` from
+  [faisalthaheem/open-lpr-models](https://huggingface.co/faisalthaheem/open-lpr-models), tag `detector-2026.10.1`
+  (commit `aed0df35ec2891c5a90437041ebeadb592cc516b`), sha256 `e5350bb2152eb44fa129b2438d1e39bbbf3c27accfe51cd893ce6e17d028d0b9`.
+- Application: [open-lpr](https://github.com/faisalthaheem/open-lpr) (Apache-2.0).
+- Architecture: [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
+  (Megvii, Apache-2.0); pretrained from COCO `yolox_tiny.pth`, then fine-tuned
+  by the open-lpr project. The weights' Apache-2.0 status rests on the open-lpr
+  repository licence and its NOTICE, which state that YOLOX code and its
+  same-repository release weights are Apache-2.0 and that no Ultralytics
+  weights were used.
+
+## Modifications
+
+The input and output contract is adapted in the graph; the upstream weights are
+untouched. The input `images` takes **RGB float in [0,1]** (NCHW,
+1x3x640x640); `Mul(255)` then `Gather([2,1,0], axis=1)` give the upstream
+graph its BGR 0..255 tensor. The upstream output `[1, 8400, 6]`
+(cx, cy, w, h, obj, cls) is reshaped to `output0 [1, 5, 8400]`
+(cx, cy, w, h, obj*cls). The OpenVINO and CoreML files are converted from that
+edited ONNX. Built by `scripts/build-yolox-plate-model.py` in the CamStack
+repository; see `SOURCE.md`.
+
+## Training data
+
+**Unknown terms.** Per the upstream NOTICE and manifest: an annotated
+licence-plate corpus (Simanno) plus community-contributed photographs,
+pseudo-labelled by a teacher model. The training imagery is not published; the
+upstream NOTICE states the community photographs contain real licence plates,
+faces and vehicles and were "uploaded without consent obtained".
+
+## Licence
+
+Apache-2.0. The upstream `LICENSE` and `NOTICE` are reproduced verbatim in this
+folder; the NOTICE must accompany any redistribution (Apache-2.0 §4(d)).

plateDetection/yolox-tiny-plate/SOURCE.md

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+# Source of plateDetection/yolox-tiny-plate
+
+| | |
+| --- | --- |
+| Upstream repository | https://huggingface.co/faisalthaheem/open-lpr-models |
+| Upstream tag / commit | `detector-2026.10.1` / `aed0df35ec2891c5a90437041ebeadb592cc516b` |
+| Upstream file | `plate_yolox_tiny_640.onnx`, 20183226 bytes, sha256 `e5350bb2152eb44fa129b2438d1e39bbbf3c27accfe51cd893ce6e17d028d0b9` |
+| Upstream LICENSE | sha256 `c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4` (copied here verbatim) |
+| Upstream NOTICE | sha256 `8fe7b8450e295ba3a1aae6359cee0cbe69142d2a8b8a395b4ddfb28ad8154fd2` (copied here verbatim) |
+| Weights licence | Apache-2.0 |
+| Training data | not published; terms UNKNOWN (see README.md) |
+
+Rebuild every file in this folder from the pinned upstream file:
+
+```
+python3 scripts/build-yolox-plate-model.py <work-dir>
+```
+
+in the CamStack repository (https://github.com/camstack/server). The script
+downloads the upstream ONNX by commit and refuses it if the sha256 differs,
+applies the graph edit described in README.md, converts the edited ONNX to
+OpenVINO IR (fp16 weights) and CoreML (fp16) with CamStack's `convert_lib.py`,
+and checks that the edit is exact and that the three runtimes return the same
+detections.

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