The images were normalized, and 50000 were used for training. The images were initially fed through a convolutional layer with 64 channels, a kernel of size 7 × 7 with a stride of 2, and a padding of 3. Then, the ResNet18 12 architecture was used up to the average pooling operation because the images are grayscale. On top of this convolutional encoder, an MLP with batch normalization, ReLU as the activation function, 64 hidden dimensions, and 2 layers were used for classification. RMSProp was used with a learning rate of 0.005 as an optimizer and a batch size of 256. The other parameters of RMSProp were chosen as the default implementation of torch. 39