To access features extracted by OverFeat, you first need to set up the OverFeat model in your deep learning framework (e.g., TensorFlow, PyTorch, or Caffe). Load a pre-trained OverFeat model and process the input image through the network.
Typically, features are accessed by extracting outputs from intermediate layers, such as convolutional layers, which capture spatial patterns and high-level representations. Use framework-specific functions like PyTorch’s forward hooks or TensorFlow’s model.layers to retrieve these features.
These features can then be used for downstream tasks, such as classification, object detection, or fine-tuning on a custom dataset. Ensure input images are preprocessed (e.g., resized and normalized) according to the model’s requirements.