![]() For example, you might need to resize an image orĬhange the image format to be compatible with the model. Raw input data for the model generally does not match the input data formatĮxpected by the model. tflite model into memory, which contains the model's TensorFlow Lite inference typically follows the following steps: This page describes how to access to the TensorFlow Lite interpreter and performĪn inference using C++, Java, and Python, plus links to other resources for each The interpreter uses a static graph ordering and a custom (less-dynamic) memoryĪllocator to ensure minimal load, initialization, and execution latency. The TensorFlow Lite interpreter is designed to be lean and fast. Inference with a TensorFlow Lite model, you must run it through an ![]() ![]() On-device in order to make predictions based on input data. The term inference refers to the process of executing a TensorFlow Lite model
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