The batch size refers to the number of images that a model processes during each training step. It is a hyperparameter that can be tuned to optimize the training process. Adjusting the batch size has its trade-offs.
On one hand, increasing the batch size can accelerate the training process, as more data is processed simultaneously, potentially leading to faster convergence. On the other hand, it also results in higher memory consumption, which might pose challenges for systems with limited memory resources.
Additionally, the batch size can impact the model's overall accuracy. Selecting an appropriate batch size is not a straightforward task; it requires a delicate balance and is influenced by multiple factors, such as the size of the dataset, the complexity of the model, and the available computing resources. Therefore, it is often beneficial to experiment with different batch sizes, both larger and smaller, to find the optimal value for a specific training scenario.