CNN: Working With Images: Summary Steps

1. Data Preparation: 
1. Make sure all images are of the same resolution.
2. Organize images into folders based on the class being predicted i.e, a folder for each class.
   
2. Data Pre-processing: Morphological Operations
1. Thresholding on the image - convert it from a grey image to a binary image.
2. Look at Erosion, Dilation, Opening, Closing.
   
3. Data Pre-processing: Normalisation
1. Divide by 255 (or) 
2. Divide by max-min (or)
3. Divide based on percentile (to account for outliers)
   
4. Data Pre-Processing: Augmentation
1. Two types of transformations for augmentation - linear and affine.
2. Different ways to augment - translation, rotation, scaling, etc.
3. Adds variability to add to train the model better.
   
5. Model Building
1. Run ablation experiments
2. Overfit on a smaller version of the training set
3. Hyperparameter tuning
4. Model training and evaluation