About fine tuning with different architecture

[jackiechensuper]

Hi guys,
If I want to classify 100 objects and use the convolution and max-pooling layers of pre-trained Imagenet model, I only change the imagenet.prototxt with different outputs in fully-connected network. How to initialize the network with pre-trained imagenet model in previous layers while latter layer randomized ?
Thanks

[kloudkl]

Please take a look at #31. You may consider searching the issues first before opening a new issue.

The secret is in Net::CopyTrainedLayersFrom. In my opinion, all you need to do is to define your desired network and name the layers that you want to copy from a pre-trained model with the names of the corresponding source layers to be copied from. Other layers will be initialized by the random fillers that you specify.

[shelhamer]

@kloudkl is right. See the finetuning slide in the Caffe presentation for a miniature example.