I am trying to train CNN model with 10000 dataset with input size of 64x64x3 pixels with batch size of 16. The training time I got was 44s. Comparing it with other model, I got extremely low training time.
Kudirat Oyewumi Jimoh Here are some approaches you may take to shorten the training duration of your CNN model:
1. Use smaller model designs since larger models take longer to train. To shorten training time, use a smaller model with fewer layers and neurons.
2. Use a GPU for training: Training on a GPU may be much faster than training on a CPU.
3. Boost the batch size: Increasing the batch size can increase parallelism and reduce the time per iteration.
4. Data augmentation: By reducing the number of photos in the training set, the training process may be sped up.
5. Early stopping: Preventing the model from overfitting and reducing the number of superfluous epochs may be accomplished by early halting.
6. Transfer learning: Transfer learning can reduce the amount of training time required by using pre-trained weights as a starting point.
7. Hyperparameter tuning: Fine-tuning hyperparameters such as learning rate, optimizer, and number of epochs can help improve training time and accuracy.
It's important to keep in mind that a lower training time does not always equate to a better model. The trade-off between training time and accuracy should be considered when selecting the optimal configuration for your use case.
There are several ways to reduce the training time of a CNN model:
in order to reduce the time of training:
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