euro-dashcam-vehicle-classifier

European Dashcam Vehicle Classifier (v19)

Privacy-first dashcam vehicle recognition: detects and identifies make + model + generation of European cars, fully offline on Apple Silicon.

Model

• Architecture: ConvNeXt-Tiny (28M params), trained from scratch
• Classes: 646 European vehicle classes (make + model, incl. generations)
• Input: 224×224 RGB image of a vehicle crop
• Output: class logits → softmax probabilities

Honest evaluation

Measured on an intersection holdout (597 real dashcam crops that no model ever trained on) — this methodology was deliberately built after discovering that naive holdout comparisons were contaminated:

Test-time augmentation (horizontal flip + scale averaging) adds ~+1pt.

Usage (PyTorch)

import torch
from torchvision import models

ckpt = torch.load("model.pt", map_location="cpu")
classes = ckpt["classes"]

model = models.convnext_tiny(weights=None)
model.classifier[2] = torch.nn.Linear(model.classifier[2].in_features, len(classes))
model.load_state_dict(ckpt["state_dict"])
model.eval()
# preprocess: resize to 224x224, normalize with ImageNet mean/std, predict

Training data

~21,300 real dashcam crops (extracted from public dashcam footage of European cities) + ~10,800 web images, 5x oversampled, from-scratch training. See the dataset card for provenance and licensing.

Privacy

The full system blurs license plates on stream + recordings by default (GDPR). This model operates on vehicle crops only.