1## TFLite accuracy library.
2
3This library provides evaluation pipelines that can be used to evaluate
4accuracy and other metrics of a model. The resulting binary can be run on
5a desktop or on a mobile device.
6
7## Usage
8The tool provides an evaluation pipeline with different stages. Each
9stage outputs a Tensorflow graph.
10A sample usage is shown below.
11
12```C++
13// First build the pipeline.
14EvalPipelineBuilder builder;
15std::unique_ptr<EvalPipeline> eval_pipeline;
16auto status = builder.WithInput("pipeline_input", DT_FLOAT)
17     .WithInputStage(&input_stage)
18     .WithRunModelStage(&run_model_stage)
19     .WithPreprocessingStage(&preprocess_stage)
20     .WithAccuracyEval(&eval)
21     .Build(scope, &eval_pipeline);
22TF_CHECK_OK(status);
23
24// Now run the pipeline with inputs and outputs.
25std::unique_ptr<Session> session(NewSession(SessionOptions()));
26TF_CHECK_OK(eval_pipeline.AttachSession(std::move(session)));
27Tensor input = ... read input for the model ...
28Tensor ground_truth = ... read ground truth for the model ...
29TF_CHECK_OK(eval_pipeline.Run(input1, ground_truth1));
30```
31For further examples, check the usage in [imagenet accuracy evaluation binary](ilsvrc/imagenet_model_evaluator.cc)
32
33## Measuring accuracy of published models.
34
35### ILSVRC (Imagenet Large Scale Visual Recognition Contest) classification task
36For measuring accuracy for [ILSVRC 2012 image classification task](http://www.image-net.org/challenges/LSVRC/2012/), the binary can be built
37using these
38[instructions.](ilsvrc/)
39