Freezing the conv layers (feature extractor) and training the linear layers (classifier) only would be a typical form of transfer learning. The idea is that the pretrained model (using the ImageNet dataset) learned to extract useful features from these images, i.e. the conv layers (with the non-linear activations, pooling layers, as well as potentially normalization layers) are detecting features from the spatial inputs, which can then be “combined” in the linear layers to classify the new dataset.