Our team was composed by:
Albert Jiménez, Marc Górriz, Adrià Romero, Michelle Compri, Denjj Osele
[Project Slides] (https://github.com/telecombcn-dl/dlcv05/blob/master/Project%20Presentation.pdf)
[Presentation] (https://docs.google.com/presentation/d/1mflcMsA4rgJHat2hwqVu6zJjPiipFmea3BJUwwSxVtI/edit#slide=id.g14a9129f29_0_0)
Build your own network to solve a classification task.
Script mnist_cnn.py:
- Options added at the beginning of the script
- We can save and load trained models
- We have the value of the loss & accuracy at each epoch
- Save total time computed
Script cifar10_cnn.py:
- Options added at the beginning of the script
- We can save and load trained models
- We have the value of the loss & accuracy at each epoch
- Save total time computed
Script mnist_cnn_3layers.py:
- Custom architecture proposed
If you are saving the model be careful when setting the paths and the name not to overwrite!
Objectives:
Study the impact in performance of:
- Data augmentation.
- Sizes of the training batches.
- Batch normalization
Overfitting:
- Force an overfitting problem.
- Investigate if regularization (eg. drop out) reduces/solves it.
Objective: Visualize filter responses
- There is the code to visualize the value of the weight as well as the output of every filter in our custom architecture on mnist database.
(Experimental code ... not working properly when fine tuning)
Train a network over CIFAR-10 and fine-tune over Terrassa Buildings 900.
Off the Shelf VGG-16
- Freeze weight in all layers but the last one, and replace it with a softmax to solve Terrassa Buildings 900.
Adquire knwoledge and insights about what is happening inside the deepdream network.
- Deepdream modify the images it is given as an input enhancing some features depending on the layer we choose to boost.
- Lower layers focus on simpler features (edges, orientation, shapes)
- Higher layers focus on concrete objects that have been seen during training