This folder contains .py files that are used to train a model to predict radius and center coordinates of circle-like shapes in a noisey image.

Files Description

• main: used to evaluate the trained model on 1000 generated samples with a noise ratio of 2.
• generate_training_data: similar to main, but used to generate the training, validation, and test data used to train the deep learning model.
• predictor: contains the deep learning model and core code that makes the inference using the generated samples in main.
• validation: code snippet used to validate the model while training.
• output.txt: contains log data for the last 1000 training epochs.

Folders Description

• checkpoints: contains 2 checkpoint files of the trained model.
• tensorboard: contrains an example tensorboard log file for some of the training steps.

Problem Formulation

In order to predict the center coordinates and radius of a circle, a deep learning model can be trained to produce three outputs that are >=0 since the radius and coordinates in image are always positive. To train the model to make such prediction a loss function needs to be formulated. Since we are measuring the difference between prediction and ground truth, this means we are dealing with a regression problem and a typical regression loss function that can be used is L2 loss with slight modification to include multiple variables to optimize for as shown below.

• Loss = Minimize(radius',xcoord',ycoord') for {(radius' - radius)^2 + (xcoord' - xcoord)^2 + (ycoord' - ycoord)} while (radius',xcoord',ycoord')>=0

Simplist use [this will make predictions]

• python main.py