In this session, you learnt to set-up a typical end-to-end pipeline for training CNNs. Specifically, you learnt the following:
Data Preprocessing
Morphological Transformations: This refers to changing the shape and size of images. The typical transformations are erosion, dilation, opening and closing.
Augmentation: Refers to making changes related to rotation, translation, shearing, etc. Augmentation is often used in image-based deep learning tasks to increase the amount and amount and variance of training data. Augmentation should only be done on the training set, never on the validation set.
Normalisation: Refers to rescaling the pixel values so that they lie within a confined range. One of the reasons to do this is to help with the issue of propagating gradients.
Network Building
Choosing the architecture: For this demo, we used the ‘ResNet’ architecture. Its biggest upside is that the ‘skip connections’ mechanism allows very deep networks.
Ablation Experiments: These refer to taking a small chunk of data and running your model on it – this helps in figuring out if the model is running at all.
Overfitting on Training Data: This tells you whether the model is behaving as expected or not.
Metrics: Depending on the situation, we choose the appropriate metrics. For binary classification problems, problems, AUG is usually the best metric.
Hyperparameter tuning: We tune hyperparameters such as the learning rate, augmentation of the network if we have already tried tuning all other hyperparameters.
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