Missed TensorFlow World? Check out the recap. Learn more

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.