This repository contains a YOLOv11m model trained to detect pinball scores in images. The model aims to facilitate the segmentation of pinball scores for Optical Character Recognition (OCR) tasks.
Dataset
Images: 1,100 hand-annotated images.
Annotations: Bounding boxes around pinball scores.
Training Details
The model was trained over 100 epochs using an A100 GPU with 40GB VRAM.
Performance Metrics at Final Epoch
Validation Metrics
Class Images Instances P R mAP50 mAP50-95
100%|██████████| 7/7 [00:01<00:00, 4.28it/s]
all 152 187 0.948 0.966 0.986 0.683
Precision (P): 0.948
Recall (R): 0.966
Mean Average Precision at IoU=0.50 (mAP50): 0.986
Mean Average Precision at IoU=0.50:0.95 (mAP50-95): 0.683
Purpose
The primary goal of this model is to detect pinball scores in any image to enable segmentation for OCR processing.
