The LoRa training session is a comprehensive program designed to provide a deep understanding of LoRa technology. The training involves processing a certain number of images, each undergoing a specified number of repetitions.
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Number of Images: This refers to the total count of images that are used in the training session. Each image is processed and analyzed to extract meaningful information.
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Repetition: Each image is processed multiple times to ensure the model learns effectively. The number of repetitions can vary based on the complexity of the images and the desired accuracy of the model.
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Batch Size: This is the number of images that are processed together as a group in the training session. A larger batch size can speed up the training process, but it also requires more computational resources.
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Gradient Accumulation Steps: This is the number of steps for which the gradient descent updates are accumulated before a weight update is performed. This can be useful when the batch size is limited by memory constraints.
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Effective Batch Size: This is the product of the batch size and the gradient accumulation steps. It represents the total number of images that contribute to each weight update.
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Epochs: An epoch is a complete pass through the entire dataset. The number of epochs is the number of times the learning algorithm will work through the entire training dataset.
Each of these parameters plays a crucial role in the training process and can significantly influence the performance and accuracy of the resulting model. The optimal values for these parameters may vary depending on the specific requirements and constraints of the training session.
Created by san_vl