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.
- 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
Thanks for the well-written post and I will follow your updates regularly.
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