A TensorFlow Lite model is represented in a special efficient portable format known as FlatBuffers (identified by the .tflite file extension). This provides several advantages over TensorFlow’s protocol buffer model format such as reduced size (small code footprint) and faster inference (data is directly accessed without an extra parsing/unpacking step) that enables TensorFlow Lite to execute efficiently on devices with limited compute and memory resources.
- A TensorFlow Lite model can optionally include metadata that has a human-readable model descriptions and machine-readable data for automatic generation of pre-and post-processing pipelines during on-device inference. Refer to Add metadata for more details.
- TensorFlow Lite is a set of tools that enables on-device machine learning by helping developers run their models on mobile, embedded, and edge devices.
- Optimized for on-device machine learning, by addressing 5 key constraints: latency (there’s no round-trip to a server), privacy (no personal data leaves the device), connectivity (internet connectivity is not required), size (reduced model and binary size) and power consumption (efficient inference and a lack of network connections).
- Multiple platform support, covering
- Diverse language support, Android and iOS devices, embedded Linux, and microcontrollers.t, which include Java, Swift, Objective-C, C++, and Python.
- High performance, with hardware acceleration and model optimization.
- End-to-end for common examples, on machine learning tasks such as image classification, object detection, pose estimation, question answering, text classification, etc. on multiple platforms.