Deploying detection models is challenging due to various factors such as inference speed, camera lenses, and model selection. One critical issue is determining the confidence threshold for bounding boxes. The Optimal Confidence Plugin helps users avoid this problem by finding the best confidence threshold using F1 score instead of mAP (Mean Average Precision). This plugin considers true positives, false positives, and false negatives to determine the optimal threshold that maximizes model effectiveness during deployment while minimizing false positives. The Optimal Confidence Threshold plugin can be easily installed and used with FiftyOne datasets, improving workflows and enhancing model performance in real-world applications.