: Resize all images to the input dimensions required by your chosen model (e.g., for ResNet or for EfficientNet-B4).
: Load the model in evaluation mode and pass the images through. Extract the flattened vector from the global average pooling layer (the layer just before the final classification head). Ekipa Sara grebenom.zip
Before feeding data into a deep learning model, standardize the input: : Resize all images to the input dimensions
: If the dataset is specialized, fine-tune only the last few convolutional blocks while keeping the initial layers frozen. Ekipa Sara grebenom.zip
: Save the resulting feature space as a .npy or .h5 file. The final dimension will typically be is the number of images and