VehicleDINO

Unified multi-task vehicle recognition model — detection, type classification, make/model identification, re-identification, and license plate OCR in a single forward pass.

Architecture

• Backbone: DINOv2 ViT-B/14 (frozen, with LoRA adapters)
• Neck: SimpleFPN (768 -> 256) + HybridEncoder (AIFI + CCFM)
• Decoder: RT-DETR-style with 300 detection queries + 1 global attribute query
• Heads: 6 task-specific heads (det, type, make, model, Re-ID, OCR)

Model Variants

File	Format	Size	Notes
vehicledino_dinov2.onnx	FP32	450 MB	Full precision
vehicledino_dinov2_int8.onnx	INT8	139 MB	Quantized, 3.2x smaller

Input / Output

Input: images — float32 tensor (1, 3, 560, 560), ImageNet-normalized RGB

Outputs:

Tensor	Shape	Description
det_boxes	(1, 300, 4)	Detection boxes (cx, cy, w, h normalized)
det_classes	(1, 300, 5)	Detection class logits (car, suv, truck, bus, van)
vehicle_types	(1, 1, 8)	Vehicle type logits
makes	(1, 1, 42)	Make classification logits
models	(1, 1, 323)	Model classification logits
reid_embeds	(1, 1, 256)	L2-normalized Re-ID embedding
ocr_logits	(1, 1, 8, 37)	License plate OCR logits (8 positions, 37 chars)

Performance (Test Set)

Task	Metric	Score
Type Classification	Top-1 Accuracy	95.6%
Make Classification	Top-1 Accuracy	98.4%
Model Classification	Top-1 Accuracy	87.7%
Re-ID (VeRi-776)	mAP	61.1%
Re-ID (VeRi-776)	Rank-1	86.1%

Training Data

• Detection + Type + Re-ID: VeRi-776 (776 vehicles, 49,360 images)
• Make/Model: CompCars (42 makes, 323 models)
• OCR: CCPD-Green (Chinese license plates)

Usage with ONNX Runtime (Python)

import onnxruntime as ort
import numpy as np
from PIL import Image

session = ort.InferenceSession("vehicledino_dinov2_int8.onnx")

# Preprocess: resize to 560x560, ImageNet normalize
img = Image.open("car.jpg").resize((560, 560))
arr = np.array(img).astype(np.float32) / 255.0
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
arr = (arr - mean) / std
tensor = arr.transpose(2, 0, 1)[np.newaxis]  # (1, 3, 560, 560)

outputs = session.run(None, {"images": tensor.astype(np.float32)})

Usage in Browser (ONNX Runtime Web)

The INT8 model runs in the browser via ONNX Runtime Web with WebGPU or WASM backend.

Live demo: https://yolov11-plate-recognition.swmengappdev.workers.dev

Citation

@article{vehicledino2026,
  title={VehicleDINO: Unified Multi-Task Vehicle Recognition via DINOv2 Features},
  author={Soh, Wei Meng},
  year={2026}
}

License

Apache 2.0