Yes, you can extract a specific number of features from an image dataset using a pre-trained CNN like “SqueezeNet”. But if you directly modify any layer to output a specific number of features (like 100) it can lead to issues.
Rather than changing the network architecture, you can use dimensionality reduction techniques like “PCA” and “t-sne”.
You can follow the below mentioned steps to extract the desirable features:
- Select any layer from CNN architecture, preferably select the last layer before MLP. Since the layer closer to the output typically captures high-level, abstract features. This layer is often considered to have learnt the "latent representation" of the input data.
- Now you can apply any dimensionality reduction technique to reduce the dimensions of selected layer output to your desirable output.
Hope it helps!
