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README.md
1# TFLite Model Task Evaluation 2 3This page describes how you can check the accuracy of quantized models to verify 4that any degradation in accuracy is within acceptable limits. 5 6## Accuracy & correctness 7 8TensorFlow Lite has two types of tooling to measure how accurately a delegate 9behaves for a given model: Task-Based and Task-Agnostic. 10 11**Task-Based Evaluation** TFLite has two tools to evaluate correctness on two 12image-based tasks: - [ILSVRC 2012](http://image-net.org/challenges/LSVRC/2012/) 13(Image Classification) with top-K accuracy - 14[COCO Object Detection](https://cocodataset.org/#detection-2020) (w/ bounding 15boxes) with mean Average Precision (mAP) 16 17**Task-Agnostic Evaluation** For tasks where there isn't an established 18on-device evaluation tool, or if you are experimenting with custom models, 19TensorFlow Lite has the Inference Diff tool. 20 21## Tools 22 23There are three different binaries which are supported. A brief description of 24each is provided below. 25 26### [Inference Diff Tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/evaluation/tasks/inference_diff#inference-diff-tool) 27 28This binary compares TensorFlow Lite execution in single-threaded CPU inference 29and user-defined inference. 30 31### [Image Classification Evaluation](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification#image-classification-evaluation-based-on-ilsvrc-2012-task) 32 33This binary evaluates TensorFlow Lite models trained for the 34[ILSVRC 2012 image classification task.](http://www.image-net.org/challenges/LSVRC/2012/) 35 36### [Object Detection Evaluation](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/evaluation/tasks/coco_object_detection#object-detection-evaluation-using-the-2014-coco-minival-dataset) 37 38This binary evaluates TensorFlow Lite models trained for the bounding box-based 39[COCO Object Detection](https://cocodataset.org/#detection-eval) task. 40 41******************************************************************************** 42 43For more information visit the TensorFlow Lite guide on 44[Accuracy & correctness](https://www.tensorflow.org/lite/performance/delegates#accuracy_correctness) 45page. 46