Using a custom image data generator in Keras provides numerous advantages. Firstly, it offers unparalleled flexibility by allowing realtime augmentation and manipulation of image data, enabling onthefly modifications like rotation, scaling, flipping, and more, augmenting the dataset without physically storing the modified images. This process enhances model generalization by exposing it to diverse variations of the same data, reducing overfitting. Additionally, custom generators efficiently handle large datasets that may not fit into memory, loading batches of data as needed during training, optimizing memory usage, and enabling seamless integration with Keras models through the fit_generator() function. Moreover, custom generators empower users to incorporate complex logic or custom preprocessing steps, tailoring the data pipeline specifically to the unique requirements of the model or the dataset, thereby enhancing overall model performance and adaptability.

Dataset: https://www.kaggle.com/datasets/faiza...
ImageDataGenerator: https://www.tensorflow.org/api_docs/p...

Notebook: https://github.com/iamtekson/deeplea...

Image Enhancement tutorial:    • Image Enhancement in Remote Sensing u...  

#keras #imagesegmentation #tensorflow


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Timestamps:
0:00 Intro
0:27 Quick recap on methodology
1:57 Advantages of Custom Data Generator
6:56 About dataset
10:23 CustomDataGenerator Class implementation in Tensorflow
26:15 Testing custom data generator using flood dataset


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