Inception modules are specialized components used in convolutional neural networks that allow the model to learn from multiple filter sizes simultaneously. This design enables the network to capture different features from the input data at various spatial scales, enhancing its ability to process complex images effectively. By combining convolutional layers with different kernel sizes, inception modules improve the network's overall performance and accuracy in tasks like image classification and object detection.
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