ALPR plate detector (YOLOv8s)
Single-class license plate detector, trained as Phase 2 of fayazhussain2821/ALPR — an end-to-end pipeline that detects plates in video, reads them, validates them against Indian and German plate grammars, and logs one deduplicated row per vehicle to an Excel workbook.
Results
Evaluated on a held-out test split of 465 images the model never saw.
| Metric | Test |
|---|---|
| mAP@50 | 0.9921 |
| mAP@50-95 | 0.8377 |
| Precision | 0.9816 |
| Recall | 0.9917 |
| Inference | 4.7 ms/image (T4) · 29 ms/frame (Apple M4, MPS) |
Recall is the metric that matters for a plate pipeline. A plate the detector misses can never be read downstream — that error is unrecoverable. A false positive produces a crop, OCR emits noise, and a grammar check rejects it. The error profile is the right way round: 1 missed plate against 23 false positives across the whole test split.
Detection by plate size
| Ground-truth width | n | Precision | Recall |
|---|---|---|---|
| tiny (<32 px) | 8 | 1.0000 | 1.0000 |
| small (32–64 px) | 64 | 0.9000 | 0.9844 |
| medium (64–128 px) | 191 | 0.9598 | 1.0000 |
| large (≥128 px) | 190 | 0.9845 | 1.0000 |
Small plates were expected to cap accuracy. They do not — every plate under 32 px was found.
The test split was audited for leakage
A perceptual-hash audit found 5.8% of test images had a near-duplicate in train (consecutive video frames whose filenames the grouped-splitting logic did not recognise as frames of one clip). Re-scoring on only the 438 uncontaminated images:
| Full test (465) | Uncontaminated (438) | |
|---|---|---|
| Recall | 0.9979 | 0.9978 |
| Precision | 0.9545 | 0.9556 |
The leak was not carrying the score. Grouping was fixed so future splits cluster duplicates.
Training
| Architecture | YOLOv8s (11.1M parameters) |
| Epochs | 100 |
| Image size | 640 |
| Hardware | Google Colab T4, 1.25 h |
| Data | 3,105 images / 3,273 plates, split 2174/466/465 |
Augmentation was tuned for plates rather than for COCO: no vertical flip (a plate is never upside down), and rotation and perspective enabled (Ultralytics defaults both to zero, which under-trains the variation this task actually has).
Full arguments are in
results/args.yaml.
Ultralytics changes augmentation defaults between minor releases, so reproducing this needs the
arguments and the version that consumed them.
Usage
from ultralytics import YOLO
model = YOLO("best.pt")
results = model.predict("car.jpg", conf=0.25)
Or through the project's pipeline, which adds tracking, OCR, plate-grammar validation and Excel logging:
alpr run --source 0 --weights best.pt --out plates.xlsx
Training data and attribution
Derived from two Roboflow Universe datasets, both CC BY 4.0:
- European License Plates — 1,455 images
- Indian License Plate (NIVU) — 1,650 images
Limitations
It only finds plates. It does not read them. Recognition is a separate stage in the pipeline.
Boxes are axis-aligned, so a plate photographed obliquely comes out as a rectangle around a slanted plate. Downstream OCR cannot un-skew it without the four corners, which this model does not predict.
The training data is European and Indian. Plates from other regions may work — a detector largely learns "small bright rectangle on a vehicle" — but that is untested here.
Test images averaged 119×40 px plates. Performance on much smaller plates, heavy motion blur or night footage is not characterised by these numbers.
Intended use and responsible use
Built as a portfolio and learning project. Automatic plate recognition is surveillance technology, and a plate is personal data — under the GDPR in the EU, and under comparable regimes elsewhere. Deploying it against public traffic engages legal obligations around lawful basis, retention, and notice that are the deployer's responsibility, not the model's.
Reasonable uses: research, private property you control, and datasets you have the right to process. This model should not be used to track individuals.