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On-device ONNX object detector that finds license plates in photos. A fine-tune of PekingU/rtdetrv2r18vd (Apache-2.0) on the Open Images "Vehicle registration plate" class, exported to a fixed 1×3×640×640 ONNX graph...
Fonte do modelo
Descrição da fonte
On-device ONNX object detector that finds license plates in photos. A
fine-tune of PekingU/rtdetr_v2_r18vd
(Apache-2.0) on the Open Images "Vehicle registration plate" class, exported to a
fixed 1×3×640×640 ONNX graph that runs fully offline with ONNX Runtime.
Built for , a local-first redaction app, to suggest plate regions for blur/redaction — nothing leaves the device.
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1 fonteVerificado 16 de set.
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos| File | Purpose |
|---|---|
plate_rtdetr.onnx | the detector (fixed 1×3×640×640, ~79 MB) |
config.json | RT-DETRv2 model config (1 class: license_plate) |
preprocessor_config.json | the image-processor settings |
pixel_values: float32[1,3,640,640], RGB, scaled ×1/255, no
mean/std normalization.logits: [1,300,1] (raw, apply sigmoid) and pred_boxes:
[1,300,4] as cx,cy,w,h normalized to [0,1].pip install onnxruntime pillow numpy
import numpy as np, onnxruntime as ort
from PIL import Image
sess = ort.InferenceSession("plate_rtdetr.onnx", providers=["CPUExecutionProvider"])
img = Image.open("car.jpg").convert("RGB")
W, H = img.size
x = (np.asarray(img.resize((640, 640)), np.float32) / 255.0).transpose(2, 0, 1)[None]
logits, boxes = sess.run(None, {"pixel_values": x}) # [1,300,1], [1,300,4]
scores = 1.0 / (1.0 + np.exp(-logits[0, :, 0])) # sigmoid
keep = scores > 0.05
for (cx, cy, w, h), s in zip(boxes[0][keep], scores[keep]):
x0, y0 = (cx - w / 2) * W, (cy - h / 2) * H
x1, y1 = (cx + w / 2) * W, (cy + h / 2) * H
print(f"plate @ ({x0:.0f},{y0:.0f},{x1:.0f},{y1:.0f}) score={s:.2f}")
(RT-DETR is NMS-free; a light NMS on overlapping boxes is optional.)
Fine-tuned from PekingU/rtdetr_v2_r18vd on ~3,000 Open Images plate images
(the box-regression head kept its COCO pretraining; the classification head was
re-initialized for the single license_plate class). Trained with the Hugging
Face transformers Trainer; early-stopped where held-out loss bottomed (~epoch
8 of a 40-epoch run — later epochs overfit).
Apache-2.0, inherited from the base model.
PekingU/rtdetr_v2_r18vd) — Apache-2.0--- license: apache-2.0 library_name: transformers pipeline_tag: object-detection tags: - object-detection - license-plate - rt-detr - rtdetr_v2 - onnx - privacy - on-device base_model: PekingU/rtdetr_v2_r18vd --- # License-plate detector — RT-DETRv2 (R18), ONNX On-device **ONNX** object detector that finds **license plates** in photos. A fine-tune of [`PekingU/rtdetr_v2_r18vd`](https://huggingface.co/PekingU/rtdetr_v2_r18vd) (Apache-2.0) on the Open Images "Vehicle registration plate" class, exported to a fixed `1×3×640×640` ONNX graph that runs fully offline with ONNX Runtime. Built for **KeepItLocal Privacy**, a local-first redaction app, to suggest plate regions for blur/redaction — nothing leaves the device. ## Intended use & scope - **Use it for:** locating license-plate bounding boxes for privacy redaction. - **Suggestion, not a guarantee:** treat each detection as a box a human confirms before redacting — like all detectors it can miss or over-fire. - **Low, well-ranked scores (important):** this single-class fine-tune produces *compressed* confidence scores (the top box is typically ~0.10–0.15) but ranks them well — the highest-scoring box lands on the real plate **~92%** of the time (mean IoU 0.74 on held-out data). **Threshold low (~0.05)** and take the top detection(s). This is an RT-DETR single-class trait, not a defect. ## Files | File | Purpose | |---|---| | `plate_rtdetr.onnx` | the detector (fixed 1×3×640×640, ~79 MB) | | `config.json` | RT-DETRv2 model config (1 class: `license_plate`) | | `preprocessor_config.json` | the image-processor settings | ## I/O - **Input** `pixel_values`: `float32[1,3,640,640]`, **RGB**, scaled `×1/255`, **no** mean/std normalization. - **Outputs** `logits`: `[1,300,1]` (raw, apply sigmoid) and `pred_boxes`: `[1,300,4]` as `cx,cy,w,h` **normalized** to `[0,1]`. ## Usage (Python, ONNX Runtime) ```bash pip install onnxruntime pillow numpy ``` ```python import numpy as np, onnxruntime as ort from PIL import Image sess = ort.InferenceSession("plate_rtdetr.onnx", providers=["CPUExecutionProvider"]) img = Image.open("car.jpg").convert("RGB") W, H = img.size x = (np.asarray(img.resize((640, 640)), np.float32) / 255.0).transpose(2, 0, 1)[None] logits, boxes = sess.run(None, {"pixel_values": x}) # [1,300,1], [1,300,4] scores = 1.0 / (1.0 + np.exp(-logits[0, :, 0])) # sigmoid keep = scores >...
Source context: 31 downloads · 0 likes · Pipeline object-detection · Library transformers · Repo Topurrra/rtdetr-license-plate-detection-onnx