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tensorflow/lite/g3doc/models/trained/index.md

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+# Pre-trained models for TensorFlow Lite
+
+There are a variety of already trained, open source models you can use
+immediately with TensorFlow Lite to accomplish many machine learning tasks.
+Using pre-trained TensorFlow Lite models lets you add machine learning
+functionality to your mobile and edge device application quickly, without having
+to build and train a model. This guide helps you find and decide on trained
+models for use with TensorFlow Lite.
+
+You can start browsing TensorFlow Lite models right away based on general use
+cases in the [TensorFlow Lite Examples](../../examples) section, or browse a
+larger set of models on [TensorFlow Hub](https://tfhub.dev/s?deployment-
+format=lite).
+
+**Important:** TensorFlow Hub lists both regular TensorFlow models and
+TensorFlow Lite format models. These model formats are not interchangeable.
+TensorFlow models can be converted into TensorFlow Lite models, but that process
+is not reversible.
+
+
+## Find a model for your application
+
+Finding an existing TensorFlow Lite model for your use case can be tricky
+depending on what you are trying to accomplish. Here are a few recommended ways
+to discover models for use with TensorFlow Lite:
+
+**By example:** The fastest way to find and start using models with TensorFlow
+Lite is to browse the [TensorFlow Lite Examples](../../examples) section to find
+models that perform a task which is similar to your use case. This short catalog
+of examples provides models for common use cases with explanations of the models
+and sample code to get you started running and using them.
+
+**By data input type:** Aside from looking at examples similar to your use
+case, another way to discover models for your own use is to consider the type of
+data you want to process, such as audio, text, images, or video data. Machine
+learning models are frequently designed for use with one of these types of data,
+so looking for models that handle the data type you want to use can help you
+narrow down what models to consider. On [TensorFlow
+Hub](https://tfhub.dev/s?deployment-format=lite), you can use the **Problem
+domain** filter to view model data types and narrow your list.
+
+Note: Processing video with machine learning models can frequently be
+accomplished with models that are designed for processing single images,
+depending on how fast and how many inferences you need to perform for your use
+case. If you intend to use video for your use case, consider using single-frame
+video sampling with a model built for fast processing of individual images.
+
+The following lists links to TensorFlow Lite models on [TensorFlow
+Hub](https://tfhub.dev/s?deployment-format=lite) for common use cases:
+
+-   [Image classification](https://tfhub.dev/s?deployment-format=lite&module-type=image-classification)
+    models
+-   [Object detection](https://tfhub.dev/s?deployment-format=lite&module-type=image-object-detection)
+    models
+-   [Text classification](https://tfhub.dev/s?deployment-format=lite&module-type=text-classification)
+    models
+-   [Text embedding](https://tfhub.dev/s?deployment-format=lite&module-type=text-embedding)
+    models
+-   [Audio speech synthesis](https://tfhub.dev/s?deployment-format=lite&module-type=audio-speech-synthesis)
+    models
+-   [Audio embedding](https://tfhub.dev/s?deployment-format=lite&module-type=audio-embedding)
+    models
+
+
+## Choose between similar models
+
+If your application follows a common use case such as image classification or
+object detection, you may find yourself deciding between multiple TensorFlow
+Lite models, with varying binary size, data input size, inference speed, and
+prediction accuracy ratings. When deciding between a number of models, you
+should narrow your options based first on your most limiting constraint: size of
+model, size of data, inference speed, or accuracy.
+
+Key Point: Generally, when choosing between similar models, pick the smallest
+model to allow for the broadest device compatibility and fast inference times.
+
+If you are not sure what your most limiting constraint is, assume it is the
+size of the model and pick the smallest model available. Picking a small model
+gives you the most flexibility in terms of the devices where you can
+successfully deploy and run the model. Smaller models also typically produce
+faster inferences, and speedier predictions generally create better end-user
+experiences. Smaller models typically have lower accuracy rates, so you may need
+to pick larger models if prediction accuracy is your primary concern.
+
+
+## Sources for models
+
+Use the [TensorFlow Lite Examples](../../examples)
+section and [TensorFlow Hub](https://tfhub.dev/s?deployment-format=lite) as your
+first destinations for finding and selecting models for use with TensorFlow
+Lite. These sources generally have up to date, curated models for use with
+TensorFlow Lite, and frequently include sample code to accelerate your
+development process.
+
+### TensorFlow models
+
+It is possible to [convert](https://www.tensorflow.org/lite/models/convert) regular
+TensorFlow models to TensorFlow Lite format. For more information about
+converting models, see the [TensorFlow Lite
+Converter](https://www.tensorflow.org/lite/models/convert) documentation. You can find
+TensorFlow models on [TensorFlow Hub](https://tfhub.dev/) and in the
+[TensorFlow Model Garden](https://github.com/tensorflow/models).