Update README.md

README.md

@@ -5,8 +5,16 @@ Strike Zone Detection Model
 By Taiko IbukiArchitecture: YOLOv8-medium (yolov8m)
 Task: Object Detection 4 classes: ball, batter, pitcher, strike_zone
 
-1. Model DescriptionThis object detection model identifies and localizes key elements of a baseball pitch to automate ball-strike calls. Using YOLOv8, this model fine-tunes a previously trained dataset from Roboflow to fit four classes: ball, batter, pitcher, and strike_zone. This model is intended to help objectively evaluate pitch calls and can be used for broadcast overlay systems or umpire analytics platforms.ParameterValueArchitectureYOLOv8-medium (yolov8m)Training basisFine-tuned from ROBO ump (Roboflow Universe)TaskObject detection (4 classes)Target mAP@50> 0.85Achieved mAP@500.92Intended use cases:
+1. Model Description
+This object detection model identifies and localizes key elements of a baseball pitch to automate ball-strike calls. Using YOLOv8, this model fine-tunes a previously trained dataset from Roboflow to fit four classes: ball, batter, pitcher, and strike_zone. This model is intended to help objectively evaluate pitch calls and can be used for broadcast overlay systems or umpire analytics platforms.
 
+Architecture: YOLOv8-medium (yolov8m)
+Training basis: Fine-tuned from ROBO ump (Roboflow Universe)
+Task: Object detection (4 classes)
+Target mAP@50: > 0.85
+Achieved mAP@50: 0.92
+
+Intended use cases:
 Broadcast overlay systems displaying a real-time model-driven strike zone visualization
 Umpire analytics platforms comparing model calls vs. official calls per game
 Research and development for automated ball-strike officiating systems