Beni4/rwanda-water-meter-reading-yolov8n

Rwanda Water Meter Reading YOLOv8n

This repository contains the selected model for the Rwanda Water Meter Reading System MVP. It detects the complete meter, the reading window, and digits 0-9. The application keeps digits inside the best window and orders them from left to right to construct a reading.

Live demonstration

Test the model without installing anything:

https://huggingface.co/spaces/Beni4/rwanda-water-meter-reading-demo

Validation performance

Validation was reproduced with Ultralytics 8.4.49 at an image size of 640 on 249 validation images containing 1,589 labeled objects. Class-by-class results are available in perclassmetrics.csv, and validation_metrics.png provides assessment evidence.

Classes

The class order is also stored in classes.yaml.

Unclear digit policy

u is not a trained object class. During post-processing, a detected digit with confidence below 0.40 is returned as u. The same result can be inserted when digit spacing indicates a likely missing position. This prevents uncertain digits from being presented as reliable readings.

Files

• —best.pt: original evaluated Ultralytics YOLOv8n checkpoint.
• —best.onnx: browser and cross-platform export of best.pt.
• —reading_postprocess.py: window filtering and reading reconstruction.
• —classes.yaml: authoritative class order and u policy.
• —training_args.yaml: parameters from the selected training experiment.
• —model_metadata.yaml: dataset, training, and validation provenance.
• —metrics.json: overall validation metrics.
• —perclassmetrics.csv: class-by-class validation metrics.
• —validation_metrics.png: validation evidence.
• —CHECKSUMS.sha256: SHA-256 identities for both model artifacts.

Python usage

Install the same Ultralytics release used for validation:

~~~bash pip install ultralytics==8.4.49 ~~~

Run object detection:

~~~python from ultralytics import YOLO

model = YOLO("best.pt") results = model.predict( source="water_meter.jpg", imgsz=640, conf=0.03, )

for result in results: print(result.boxes) ~~~

Candidate confidence 0.03 retains weak digit candidates for reading post-processing. It is intentionally different from the 0.40 threshold used to convert an uncertain selected digit to u.

Training data

The selected run used 1,248 images: 329 teacher-provided meter images and a limited 919-image Roboflow digit subset. The private training images are not redistributed in this model repository. Users must verify ownership and source licenses before reusing or publishing the original datasets.

Limitations

• —Performance can decrease with blur, glare, severe perspective, occlusion, or very small digits.
• —Inverted images can change the semantic meaning of digits such as 6 and 9.
• —The ONNX export can move borderline confidence values across the u threshold; the submitted assessment checkpoint is best.pt.
• —A predicted reading should be reviewed whenever it contains u.