Transfer learning

TRANSFER LEARNING Using a pre trained network on images not in training set is known as transfer learning

DIFFERENT ARCHITECTURES WE CAN USE

THESE ARE THE TOP 1 AND TOP 2 ERROR

• The numbers such as VCG-11 where 11 are the no of layers.
• When we are using this we need to do the trade-off between accuracy and speed.
• They are massively deep.They have 100's  of hidden  layer

MODELS

USING DENSENET

• It has 121 different layers

LOADING AND ARCHITECTURE

• After loading we can see the architecture.We have features and classifiers
• The classifier has 1024 input features and 1000 output features
• Imagenet dataset has 1000 different classes.The no of output should be 1000 for these classes.
• The features would not be used and need to be freezed.

FREEZING THE FEATURES

• We need feature part static, to this we would freeze our feature parameter as shown below
• This would increase processing speed as it would not keep track of features

BUILD CLASSIFIER
We would build our classifier as shown below

ATTACH TO OUR MODEL

MOVE TO GPU

• We can move all the computing to a GPU by specifying model.cuda . This would move all the parameters and model to the GPU

• To move the tensors to the GPU, we need to use image.cuda
• To specify cpu we need to change it to image.cpu, model.cpu

COMPARISON BETWEEN CPU AND GPU

• We would get a speed of over a 100 times