# 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](https://www.tensorflow.org/lite/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](./quantization/index.md)
during [TensorFlow Lite conversion](https://www.tensorflow.org/lite/convert),
which can optimize your already-trained TensorFlow model.

See the
[post-training quantization tutorial](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_quant.ipynb)
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](optimize_further.md) with our training-time tools and dig
deeper.

# 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](https://www.tensorflow.org/lite/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](./quantization/post_training)
during [TensorFlow Lite conversion](https://www.tensorflow.org/lite/convert),
which can optimize your already-trained TensorFlow model.

See the
[post-training quantization tutorial](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_quant.ipynb)
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](optimize_further.md) with our training-time tools and dig
deeper.

Fix link to post training documentation.
Fix link to post training documentation.

PiperOrigin-RevId: 304480345
