菌落计数人工智能模型准确率评估数据集

本数据集是具有计量学意义的标准菌落图像数据，主要用于评估菌落计数AI模型的更新迭代性能，特别是验证模型在菌落识别、分割及计数精确度（如CFU误差率、假阳性/阴性率）方面的优化效果。数据包含不同形态、密度和重叠程度的菌落图像，并附带经人工标注的数据，可作为量化模型性能的基准参照。适用于微生物检测算法开发团队、计量认证机构对模型升级版本的客观评价，确保其符合行业标准（如ISO 4833-1）的计量学要求。

This dataset is a set of standard colony image data with metrological significance, primarily employed to evaluate the performance of colony counting AI models during their update and iteration processes, and specifically to validate the optimization effects of the models in terms of colony recognition, segmentation, and counting accuracy (e.g., CFU error rate, false positive rate and false negative rate). The dataset comprises colony images with varying morphologies, densities and overlap degrees, along with manually annotated data, which can serve as a benchmark reference for the quantitative evaluation of model performance. It is suitable for objective evaluation of updated model versions by microbial detection algorithm development teams and metrological certification bodies, to ensure that the models comply with the metrological requirements specified in industry standards such as ISO 4833-1.