Parkhi, andrea vedaldi, andrew zisserman, and c Key techniques like data augmentation, learning rate scheduling, and early stopping were crucial in achieving accurate and efficient training. Explore and run machine learning code with kaggle notebooks | using data from no attached data sources Table of contents image segmentation on oxford_iiit_pet dataset pet image segmentor model saving A 37 category pet dataset with roughly 200 images for each class Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals.
Input_image = tf.image.resize(datapoint['image'], (128, 128)) input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128)) input_image, input_mask = normalize(input_image, input_mask) return input_image, input_mask
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