Fig. 2: Comparative training loss convergence analysis across different deep learning models. | npj Science of Food

Fig. 2: Comparative training loss convergence analysis across different deep learning models.

From: Phenotypic feature-based identification of tea geographical origin using lightweight deep learning

Fig. 2: Comparative training loss convergence analysis across different deep learning models.

The plot displays the training loss curves averaged over the 10-fold cross-validation for the proposed Origin-Tea model and nine comparative baselines (CoAtNet, EfficientNet, GhostNetV3, InceptionNeXt, MobileNetV3, MobileNetV4, ResMLP, ResNet50, and StartNet). The X-axis represents the training epochs, and the Y-axis represents the loss value. The smooth and rapid descent of the curves indicates that all models successfully converged and were effectively optimized under the specified hyperparameters, with Origin-Tea demonstrating competitive convergence speed and stability comparable to larger architectures.

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