open-lpr models
Model artifacts for open-lpr, a
free and open-source licence plate recognition system. The application code is
in the open-lpr repository; this repository holds only the weights.
The detector is trained by this project. The recogniser and dictionary are conversions of an upstream model — see Provenance before assuming otherwise.
What is here
| artifact | role | ours? |
|---|---|---|
plate_yolox_tiny_640.onnx | plate detector, YOLOX-tiny, 640×640 | trained here |
plate_ocr_ppocrv5_mobile.onnx | plate text recogniser, CTC | upstream, converted |
plate_ocr_dict.json | character dictionary (18,383 chars) | upstream, converted |
best_ckpt.pth | detector training checkpoint | trained here |
All three runtime artifacts are required. The application refuses to start without them; see Usage.
Verify any artifact against the sha256 in manifest.json
before loading it. That file is generated from the artifact bytes themselves,
not written by hand.
Demo
Detected and read by the weights in this repository, using the project's own
public test image (open-lpr/blackbox/jeep.jpg, 640×360):
| plate box | confidence 0.875 |
| text read | 6 characters, confidence 0.99 |
| end-to-end | 110 ms (cold, CPU, onnxruntime) |
The registration is pixelated and the recognised text is masked in this image, because a model card should not republish a real plate number. The recogniser read it correctly; only the display is redacted.
What this demo does not show. The plate also carries Arabic script, which was
not transcribed — the default alphanumeric charset profile emits only
alphanumerics, so non-Latin characters are stripped by configuration, not by
failure. This is a
single image and is not evidence of accuracy; see below.
Measured accuracy
Scored on split v4 — 1,275 images, 1,143 plates, human-corrected ground truth.
Detection IoU 0.3, confidence 0.3.
| detector (this repo) | superseded model | |
|---|---|---|
| recall | 0.926 | 0.906 |
| precision | 0.897 | 0.835 |
| false positives / image | 0.095 | 0.161 |
| recall, plates ≥100px tall | 0.958 | 0.899 |
| recall, plates <40px tall | 0.832 | 0.829 |
| latency, mean / p95 | 20.8 ms / 38.6 ms | 21.7 ms / 39.6 ms |
Sample sizes matter here, because several plausible-looking differences are not real. A seed-repeat of the identical configuration on 1,275 images gave:
| axis | run-to-run spread |
|---|---|
| recall | 0.013 |
| precision | 0.013 |
| recall <40px | 0.021 |
Read against that noise floor:
- Real: precision +0.063 and recall ≥100px +0.059 — roughly 5× the spread.
- Marginal: recall +0.019 — about 1.5× the spread.
- Noise: recall <40px +0.003 — no measurable effect.
So the defensible claim is that this model is substantially better at precision and large plates, with recall and small-plate performance unchanged. A previous measurement showing a 2-point regression on small plates was an artefact of incomplete teacher labels and does not exist on corrected ground truth.
The noise floor above comes from one seed pair (n=2). It indicates scale, not a confidence interval.
Text accuracy is not measured. The public corpus annotates plate boxes and carries no transcriptions. The recogniser's accuracy on non-Latin scripts is therefore unknown — see Provenance.
Usage
The application loads these from PIPELINE_MODEL_DIR, default model/plate/,
and expects these filenames:
PIPELINE_MODEL_DIR=model/plate/
PIPELINE_DETECTOR_MODEL=plate_yolox_tiny_640.onnx
PIPELINE_OCR_MODEL=plate_ocr_ppocrv5_mobile.onnx
PIPELINE_OCR_DICT=plate_ocr_dict.json
Inference needs onnxruntime only. ROCm is not required to load the
checkpoint — only to train. CPU torch reads a .pth fine.
The three runtime artifacts are a set: the recogniser and dictionary must
come from the same export. Decoding assumes len(dict) + 2 output classes, so
substituting one without the other yields silently wrong text rather than an
error.
best_ckpt.pthis a pickle. Loading it executes constructors. Only load checkpoints you trust.
Provenance
Detector — trained here
YOLOX-tiny, Apache-2.0 for both the code and the same-repository release
weights. Pretrained from COCO yolox_tiny.pth, then trained on the Simanno
annotated plate corpus (frame-grouped split with a deliberate gap) plus
community-contributed images pseudo-labelled by a teacher model.
Ultralytics YOLO weights are AGPL-3.0 and are not used anywhere in this project. The detector is YOLOX.
The community images were pseudo-labelled by an 8B Qwen3-VL teacher. Human review found that teacher omitted roughly 7% of plates and misplaced a further batch. Val ground truth here is human-corrected; 251 training images containing unlabelled plates were dropped, because an unlabelled plate is false-negative supervision — the model is penalised for detecting something that is really there. Teacher identity is operator-attested, not verifiable from artifacts.
Recogniser and dictionary — upstream, converted by us
These were not trained by this project, and their exact upstream release was never recorded. It cannot be recovered: the ONNX graph carries no producer, version or doc string, and no PaddlePaddle installation, wheel or shell history survives on the build host.
What can be stated:
- Converted from a PP-OCR recognition model using Paddle 3.x
(graph name
PaddlePaddle Graph in PIR mode, opset 13). - PaddleOCR is Apache-2.0. That licence is asserted at project level; the specific release is unverified.
One unresolved question, flagged rather than hidden. The dictionary is
18,383 characters. PaddleOCR's stock ppocr_keys_v1.txt is 6,622 — verified
against refs release/2.7, release/2.8, v2.7.0 and v2.9.0. So this is
not a stock PaddleOCR dictionary: it is either a different recognition
variant or a custom extension. Regenerating it from stock PP-OCR would change
which characters the model can emit, and there is no labelled non-Latin data to
detect a regression. Do not regenerate it without first labelling non-Latin
plates.
Training imagery is not published
The detector was trained on community-contributed photographs of real vehicles. They are not redistributed here, because that would distribute real licence plate numbers at scale.
The tooling used to prepare them is public in lpr_app/ml/ in the open-lpr
repository, and needs no imagery to be useful.
Licence
Apache-2.0. See LICENSE and NOTICE.
The repository as a whole is Apache-2.0, but that does not mean every artifact
here is ours. The recogniser and dictionary are conversions of an upstream
Apache-2.0 project and are attributed as such in manifest.json.