One way it can fail is that your model might overfit your data if number of parameters you are training are way more than data you are using for training and if

One way it can fail is that your model might overfit your data if number of parameters you are training are way more than data you are using for training and if regularization techniques are not used. We don't arbitrarily cut-off layers from pretrained model. We check at what layer, output features are not specific to problem for which pretrained model was trained. At this layer, you can stitch a nanonet and build a model for your data