# TFLite Model Task Evaluation

This page describes how you can check the accuracy of quantized models to verify
that any degradation in accuracy is within acceptable limits.

## Accuracy & correctness

TensorFlow Lite has two types of tooling to measure how accurately a delegate
behaves for a given model: Task-Based and Task-Agnostic.

**Task-Based Evaluation** TFLite has two tools to evaluate correctness on two
image-based tasks: - [ILSVRC 2012](http://image-net.org/challenges/LSVRC/2012/)
(Image Classification) with top-K accuracy -
[COCO Object Detection](https://cocodataset.org/#detection-2020) (w/ bounding
boxes) with mean Average Precision (mAP)

**Task-Agnostic Evaluation** For tasks where there isn't an established
on-device evaluation tool, or if you are experimenting with custom models,
TensorFlow Lite has the Inference Diff tool.

## Tools

There are three different binaries which are supported. A brief description of
each is provided below.

### [Inference Diff Tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/evaluation/tasks/inference_diff#inference-diff-tool)

This binary compares TensorFlow Lite execution in single-threaded CPU inference
and user-defined inference.

### [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)

This binary evaluates TensorFlow Lite models trained for the
[ILSVRC 2012 image classification task.](http://www.image-net.org/challenges/LSVRC/2012/)

### [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)

This binary evaluates TensorFlow Lite models trained for the bounding box-based
[COCO Object Detection](https://cocodataset.org/#detection-eval) task.

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For more information visit the TensorFlow Lite guide on
[Accuracy & correctness](https://www.tensorflow.org/lite/performance/delegates#accuracy_correctness)
page.