Get started with TensorFlow model optimization

1. Choose the best model for the task

Depending on the task, you will need to make a tradeoff between model complexity and size. If your task requires high accuracy, then you may need a large and complex model. For tasks that require less precision, it is better to use a smaller model because they not only use less disk space and memory, but they are also generally faster and more energy efficient.

2. Pre-optimized models

See if any existing TensorFlow Lite pre-optimized models provide the efficiency required by your application.

3. Post-training tooling

If you cannot use a pre-trained model for your application, try using TensorFlow Lite post-training quantization tools during TensorFlow Lite conversion, which can optimize your already-trained TensorFlow model.

See the post-training quantization tutorial to learn more.

Next steps: Training-time tooling

If the above simple solutions don't satisfy your needs, you may need to involve training-time optimization techniques. Optimize further with our training-time tools and dig deeper.