Golden Phoebe wood texture imagery data and its training process with convolutional neural networks

To delve deeper into the subjective evaluation words and scores of Phoebe wood grain, and to extract the imagery features of Golden Phoebe wood, the entire dataset was randomly divided into a training set (96%) and a test set (4%), containing 6840 and 285 samples, respectively. Each training sample image was subjected to random horizontal flipping, vertical flipping, and rotation operations before being input into the convolutional neural network.The images used as training data were processed by randomly initializing the weights of the neural network, employing the MSE Loss function as the loss function, and undergoing linear regression training.

The entire convolutional neural network model was trained for 35 epochs, and finally, the validation data was used for testing. Figure 10 depicts the graph of Loss rate changes during the training period. After 35 epochs of training, the convolutional neural network has reached a state of convergence, demonstrating the usability of the model.