Beschreibung
The paper discusses a project aimed at automating the differentiation of mold samples to ensure clean air in offices and production facilities, using deep neural networks to reduce the time and cost of manual differentiation. Two classification models, EfficientNet V2 and Normalization-Free Net (NFNet), were trained on artificially created data to identify five classes of mold plus an "other" category. The NFNet model, trained on unpadded images, achieved superior performance with an accuracy of 85.9%, precision of 83.7%, and recall of 78.9%. The semi-supervised approach employed reduced manual differentiation time by 50%, making the process more efficient and cost-effective. Grad-CAM was used for model interpretability, ensuring transparent decision-making.
