The filename zad1.zip (short for zadanie 1 , or "task 1" in several Slavic languages) suggests this is a specific homework assignment or project file. In this context, "deep feature" usually implies one of the following tasks:
: Using a pre-trained model (like VGG16, ResNet, or AlexNet) to convert an image into a numerical vector (a "deep feature") for use in a simpler classifier like an SVM or k-Nearest Neighbors. zad1.zip
: Identifying which specific deep features are most relevant for a particular prediction task, often referred to as Deep Feature Screening (DeepFS) . 3. Implementation Example The filename zad1
: Reusing layers from a deep model to initialize a new task, where the "deep features" serve as the foundation for learning. Copied to clipboard
If you are working with Python (common for these tasks), deep features are typically extracted by removing the final classification layer of a model:
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