: Discard the final output layer (the part that makes the actual prediction like "dog" or "cat") to isolate the feature extractor .
: Feed your raw data through the model. Instead of receiving a label, you will receive a numerical feature vector from a deep layer (like the flatten or fc6 layer). 1638117478zy4xe02:09:50 Min
To create a "deep feature," you typically use a pre-trained to extract high-level representations from raw data, such as images or text, rather than manually engineering features . These features are pulled from the intermediate or "bottleneck" layers of the network, which capture complex patterns that human-defined rules often miss. Steps to Create a Deep Feature : Discard the final output layer (the part
: Choose a high-performing architecture like VGG16 , ResNet50 , or MobileNetV2 that has been trained on a massive dataset (e.g., ImageNet). To create a "deep feature," you typically use
: Use these extracted vectors as the input for a simpler machine learning model, such as an SVM or Random Forest , to perform your specific task. Extract Features from Image using Pretrained Model | Python