Final model as a result of graduation work
This model was achieved after series of experiments.
Model is described with its parts and training hyperparameters (for reproducing results also need to mention images and augmentation parameters), namely:
Architecture: Unet
Encoder/backbone: EffecientNet-b4
Loss: BinaryCrossEntropy
Optimizer: RMSProp(lr = 8.57e-4, eps = 8.14e-6, mu = 0.479)
Batchsize: 8
Cropsize: 384 px
Augmentation: HorizontalFlip(probability = 0.736), VerticalFlip(probability = 0.277), Rotate(limit=30, probability = 0.735), ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2, probability = 0.703)
Markup: Circles and Ellipses separately
This models are trained, validated and tested on data organization "equal" due to the higher variability of data in sets in comparison with "certain" and "random" data organizations.