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-## TFLite accuracy library.
-
-This library provides evaluation pipelines that can be used to evaluate
-accuracy and other metrics of a model. The resulting binary can be run on
-a desktop or on a mobile device.
-
-## Usage
-The tool provides an evaluation pipeline with different stages. Each
-stage outputs a Tensorflow graph.
-A sample usage is shown below.
-
-```C++
-// First build the pipeline.
-EvalPipelineBuilder builder;
-std::unique_ptr<EvalPipeline> eval_pipeline;
-auto status = builder.WithInput("pipeline_input", DT_FLOAT)
-     .WithInputStage(&input_stage)
-     .WithRunModelStage(&run_model_stage)
-     .WithPreprocessingStage(&preprocess_stage)
-     .WithAccuracyEval(&eval)
-     .Build(scope, &eval_pipeline);
-TF_CHECK_OK(status);
-
-// Now run the pipeline with inputs and outputs.
-std::unique_ptr<Session> session(NewSession(SessionOptions()));
-TF_CHECK_OK(eval_pipeline.AttachSession(std::move(session)));
-Tensor input = ... read input for the model ...
-Tensor ground_truth = ... read ground truth for the model ...
-TF_CHECK_OK(eval_pipeline.Run(input1, ground_truth1));
-```
-For further examples, check the usage in [imagenet accuracy evaluation binary](ilsvrc/imagenet_model_evaluator.cc)
-
-## Measuring accuracy of published models.
-
-### ILSVRC (Imagenet Large Scale Visual Recognition Contest) classification task
-For measuring accuracy for [ILSVRC 2012 image classification task](http://www.image-net.org/challenges/LSVRC/2012/), the binary can be built
-using these
-[instructions.](ilsvrc/)