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

Metric	Value
Precision	96.52%
Recall	94.95%
mAP@50	97.21%
mAP@50-95	66.88%

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 per_class_metrics.csv, and validation_metrics.png provides assessment evidence.

Classes

ID	Class	ID	Class
0	meter	6	4
1	window	7	5
2	0	8	6
3	1	9	7
4	2	10	8
5	3	11	9

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.
• per_class_metrics.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:

pip install ultralytics==8.4.49

Run object detection:

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.