Zad1.zip -

import torch import torchvision.models as models # Load a pre-trained model model = models.resnet50(pretrained=True) # Remove the last fully connected layer to get features feature_extractor = torch.nn.Sequential(*(list(model.children())[:-1])) # 'output' will be the deep feature vector for an input image # output = feature_extractor(input_image) Use code with caution. Copied to clipboard

: Applying techniques like PCA or Autoencoders to compress high-dimensional deep features into a more manageable "compact feature vector". zad1.zip

The reference to and "deep feature" typically appears in the context of academic or technical assignments (often in computer vision or machine learning) where a student or developer is tasked with extracting or manipulating high-level representations from data. 1. What is a "Deep Feature"? import torch import torchvision

If you are working with Python (common for these tasks), deep features are typically extracted by removing the final classification layer of a model: zad1.zip