The j-IR-vis model, based on ResNet50 , was trained with all layers unfrozen to allow for optimal prediction of functional groups. An MLP was attached to the ResNet50 backbone, incorporating a dropout layer with a rate of 0.5 to prevent overfitting. The dataset was split into 80% training and 20% testing sets. To optimize the training process, we employed a learning rate scheduler (tf.keras.callbacks.ReduceLROnPlateau) with a reduction factor of 0.2, patience of 4 epochs, and a minimum learning rate of 1e-6. The ReduceLROnPlateau scheduler dynamically adjusts the learning rate by monitoring the validation loss and reducing the learning rate when this metric has stopped improving. This adaptive approach helps in fine-tuning the model's performance, especially in later stages of training by responding to plateaus in the validation performance. L2 regularization was applied with a coefficient of 0.001 to further mitigate overfitting. The initial learning rate was set to 1e-4, with a weight decay of 0.1. To improve model generalization and handling of uncertain labels, we implemented label smoothing with a factor of 0.1, parameterized as smoothed_labels = labels • (1smoothing) + smoothing . The IR spectroscopy images underwent several preprocessing steps to enhance their quality and consistency for model training. Initially, all images were rescaled to a uniform size of 512x512 pixels, ensuring consistent input dimensions for the model. The rescaled images were then converted to grayscale, reducing the complexity of the input data while retaining the essential spectral information. To maintain compatibility with standard image processing architectures, the single-channel grayscale images were expanded to three channels by repeating the intensity values across all axes. Further augmentation and normalization were performed using the Python library "albumentations". This process involved applying random brightness and contrast adjustments, rotation, Gaussian noise, and Gaussian blur to increase the diversity of the training data and improve model robustness. Finally, the images were normalized to have a mean of 0.8 and a standard deviation of 0.2. This normalization approach worked best for the dataset, highlighting the relevant peaks in the IR image.