Fix broken links and spelling errors
PiperOrigin-RevId: 236786449
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
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commit
b71b8ecf64
@ -64,19 +64,19 @@ tflite_convert \
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--saved_model_dir=/tmp/saved_model
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```
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[SavedModel](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators)
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[SavedModel](https://www.tensorflow.org/guide/saved_model.md#using_savedmodel_with_estimators)
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has fewer required flags than frozen graphs due to access to additional data
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contained within the SavedModel. The values for `--input_arrays` and
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`--output_arrays` are an aggregated, alphabetized list of the inputs and outputs
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in the [SignatureDefs](https://www.tensorflow.org/serving/signature_defs) within
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in the [SignatureDefs](../../serving/signature_defs.md) within
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the
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[MetaGraphDef](https://www.tensorflow.org/guide/saved_model#apis_to_build_and_load_a_savedmodel)
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[MetaGraphDef](https://www.tensorflow.org/saved_model.md#apis_to_build_and_load_a_savedmodel)
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specified by `--saved_model_tag_set`. As with the GraphDef, the value for
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`input_shapes` is automatically determined whenever possible.
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There is currently no support for MetaGraphDefs without a SignatureDef or for
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MetaGraphDefs that use the [`assets/`
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directory](https://www.tensorflow.org/guide/saved_model#structure_of_a_savedmodel_directory).
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directory](https://www.tensorflow.org/guide/saved_model.md#structure_of_a_savedmodel_directory).
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### Convert a tf.Keras model <a name="keras"></a>
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@ -38,7 +38,7 @@ The following flags specify optional parameters when using SavedModels.
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Specifies a comma-separated set of tags identifying the MetaGraphDef within
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the SavedModel to analyze. All tags in the tag set must be specified.
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* `--saved_model_signature_key`. Type: string. Default:
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[DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants).
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`tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`.
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Specifies the key identifying the SignatureDef containing inputs and
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outputs.
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@ -241,8 +241,8 @@ interpreter.allocate_tensors()
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In order to run the latest version of the TensorFlow Lite Converter Python API,
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either install the nightly build with
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[pip](https://www.tensorflow.org/install/pip) (recommended) or
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[Docker](https://www.tensorflow.org/install/docker), or
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[build the pip package from source](https://www.tensorflow.org/install/source).
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[Docker](https://www.tensorflow.org/install/docker.md), or
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[build the pip package from source](https://www.tensorflow.org/install/source.md).
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### Converting models from TensorFlow 1.12 <a name="pre_tensorflow_1.12"></a>
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@ -3,7 +3,7 @@
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This document describes how to build TensorFlow Lite iOS library. If you just
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want to use it, the easiest way is using the TensorFlow Lite CocoaPod releases.
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See [TensorFlow Lite iOS Demo](demo_ios.md) for examples.
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See [TensorFlow Lite iOS Demo](ios.md) for examples.
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## Building
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@ -11,17 +11,17 @@ detailed documentation for the topic or file a
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The TensorFlow Lite converter supports the following formats:
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* SavedModels:
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[TFLiteConverter.from_saved_model](convert/python_api.md#exporting_a_savedmodel_)
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[TFLiteConverter.from_saved_model](../convert/python_api.md#exporting_a_savedmodel_)
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* Frozen GraphDefs generated by
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[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py):
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[TFLiteConverter.from_frozen_graph](convert/python_api#exporting_a_graphdef_from_file_)
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[TFLiteConverter.from_frozen_graph](../convert/python_api.md#exporting_a_graphdef_from_file_)
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* tf.keras HDF5 models:
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[TFLiteConverter.from_keras_model_file](convert/python_api#exporting_a_tfkeras_file_)
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[TFLiteConverter.from_keras_model_file](../convert/python_api.md#exporting_a_tfkeras_file_)
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* tf.Session:
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[TFLiteConverter.from_session](python_api#exporting_a_graphdef_from_tfsession_)
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[TFLiteConverter.from_session](../convert/python_api.md#exporting_a_graphdef_from_tfsession_)
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The recommended approach is to integrate the
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[Python converter](convert/python_api.md) into your model pipeline in order to
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[Python converter](../convert/python_api.md) into your model pipeline in order to
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detect compatibility issues early on.
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#### Why doesn't my model convert?
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@ -69,7 +69,7 @@ bazel run //tensorflow/lite/tools:visualize model.tflite visualized_model.html
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#### Why are some operations not implemented in TensorFlow Lite?
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In order to keep TensorFlow Lite lightweight, only certain operations were used
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in the converter. The [Compatibility Guide](tf_ops_compatibility.md) provides a
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in the converter. The [Compatibility Guide](ops_compatibility.md) provides a
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list of operations currently supported by TensorFlow Lite.
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If you don’t see a specific operation (or an equivalent) listed, it's likely
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@ -78,34 +78,34 @@ GitHub [issue #21526](https://github.com/tensorflow/tensorflow/issues/21526).
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Leave a comment if your request hasn’t already been mentioned.
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In the meanwhile, you could try implementing a
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[custom operator](custom_operators.md) or using a different model that only
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[custom operator](ops_custom.md) or using a different model that only
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contains supported operators. If binary size is not a constraint, try using
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TensorFlow Lite with [select TensorFlow ops](using_select_tf_ops.md).
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TensorFlow Lite with [select TensorFlow ops](ops_select.md).
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#### How do I test that a TensorFlow Lite model behaves the same as the original TensorFlow model?
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The best way to test the behavior of a TensorFlow Lite model is to use our API
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with test data and compare the outputs to TensorFlow for the same inputs. Take a
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look at our [Python Interpreter example](convert/python_api.md) that generates
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look at our [Python Interpreter example](../convert/python_api.md) that generates
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random data to feed to the interpreter.
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## Optimization
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#### How do I reduce the size of my converted TensorFlow Lite model?
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[Post-training quantization](performance/post_training_quantization.md) can be
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[Post-training quantization](../performance/post_training_quantization.md) can be
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used during conversion to TensorFlow Lite to reduce the size of the model.
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Post-training quantization quantizes weights to 8-bits of precision from
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floating-point and dequantizes them during runtime to perform floating point
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computations. However, note that this could have some accuracy implications.
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If retraining the model is an option, consider
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[Quantization-aware training](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/quantize/README.md).
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[Quantization-aware training](https://github.com/tensorflow/tensorflow/tree/r1.13/tensorflow/contrib/quantize).
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However, note that quantization-aware training is only available for a subset of
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convolutional neural network architectures.
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For a deeper understanding of different optimization methods, look at
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[Model optimization](performance/model_optimization.md).
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[Model optimization](../performance/model_optimization.md).
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#### How do I optimize TensorFlow Lite performance for my machine learning task?
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@ -113,7 +113,7 @@ The high-level process to optimize TensorFlow Lite performance looks something
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like this:
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* *Make sure that you have the right model for the task.* For image
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classification, check out our [list of hosted models](models.md).
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classification, check out our [list of hosted models](hosted_models.md).
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* *Tweak the number of threads.* Many TensorFlow Lite operators support
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multi-threaded kernels. You can use `SetNumThreads()` in the
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[C++ API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/interpreter.h#L345)
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@ -124,12 +124,12 @@ like this:
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Networks API, call
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[`UseNNAPI`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/interpreter.h#L343)
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on the interpreter. Or take a look at our
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[GPU delegate tutorial](performance/gpu.md).
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[GPU delegate tutorial](../performance/gpu.md).
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* *(Advanced) Profile Model.* The Tensorflow Lite
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[benchmarking tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark)
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has a built-in profiler that can show per-operator statistics. If you know
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how you can optimize an operator’s performance for your specific platform,
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you can implement a [custom operator](custom_operators.md).
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you can implement a [custom operator](ops_custom.md).
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For a more in-depth discussion on how to optimize performance, take a look at
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[Best Practices](performance/best_practices.md).
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[Best Practices](../performance/best_practices.md).
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@ -35,7 +35,7 @@ by suggesting contextually relevant messages. The model is built specifically fo
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memory constrained devices, such as watches and phones, and has been successfully
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used in Smart Replies on Android Wear. Currently, this model is Android-specific.
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These pre-trained models are [available for download](models.md).
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These pre-trained models are [available for download](hosted_models.md).
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### Re-train Inception-V3 or MobileNet for a custom data set
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@ -63,24 +63,24 @@ the framework. See
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to create file for the custom model.
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TensorFlow Lite currently supports a subset of TensorFlow operators. Refer to
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the [TensorFlow Lite & TensorFlow Compatibility Guide](tf_ops_compatibility.md)
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the [TensorFlow Lite & TensorFlow Compatibility Guide](ops_compatibility.md)
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for supported operators and their usage. This set of operators will continue to
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grow in future Tensorflow Lite releases.
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## 2. Convert the model format
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The [TensorFlow Lite Converter](convert/index.md) accepts the following file
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The [TensorFlow Lite Converter](../convert.md) accepts the following file
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formats:
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* `SavedModel` — A `GraphDef` and checkpoint with a signature that labels
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input and output arguments to a model. See the documentation for converting
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SavedModels using [Python](convert/python_api.md#basic_savedmodel) or using
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the [command line](convert/cmdline_examples.md#savedmodel).
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SavedModels using [Python](../convert/python_api.md#basic_savedmodel) or using
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the [command line](../convert/cmdline_examples.md#savedmodel).
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* `tf.keras` - A HDF5 file containing a model with weights and input and
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output arguments generated by `tf.Keras`. See the documentation for
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converting HDF5 models using
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[Python](convert/python_api.md#basic_keras_file) or using the
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[command line](convert/cmdline_examples.md#keras).
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[Python](../convert/python_api.md#basic_keras_file) or using the
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[command line](../convert/cmdline_examples.md#keras).
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* `frozen tf.GraphDef` — A subclass of `tf.GraphDef` that does not contain
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variables. A `GraphDef` can be converted to a `frozen GraphDef` by taking a
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checkpoint and a `GraphDef`, and converting each variable into a constant
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@ -154,9 +154,9 @@ the arguments for specifying the output nodes for inference in the
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### Full converter reference
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The [TensorFlow Lite Converter](convert/index.md) can be
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[Python](convert/python_api.md) or from the
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[command line](convert/cmdline_examples.md). This allows you to integrate the
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The [TensorFlow Lite Converter](../convert.md) can be
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[Python](../convert/python_api.md) or from the
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[command line](../convert/cmdline_examples.md). This allows you to integrate the
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conversion step into the model design workflow, ensuring the model is easy to
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convert to a mobile inference graph.
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@ -195,15 +195,15 @@ The open source Android demo app uses the JNI interface and is available
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[on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/java/demo/app).
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You can also download a
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[prebuilt APK](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk).
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See the <a href="./demo_android.md">Android demo</a> guide for details.
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See the <a href="./android.md">Android demo</a> guide for details.
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The <a href="./android_build.md">Android mobile</a> guide has instructions for
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The <a href="./android.md">Android mobile</a> guide has instructions for
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installing TensorFlow on Android and setting up `bazel` and Android Studio.
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### iOS
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To integrate a TensorFlow model in an iOS app, see the
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[TensorFlow Lite for iOS](ios.md) guide and <a href="./demo_ios.md">iOS demo</a>
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[TensorFlow Lite for iOS](ios.md) guide and <a href="./ios.md">iOS demo</a>
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guide.
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#### Core ML support
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@ -218,9 +218,9 @@ devices. To use the converter, refer to the
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### ARM32 and ARM64 Linux
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Compile Tensorflow Lite for a Raspberry Pi by following the
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[RPi build instructions](rpi.md) Compile Tensorflow Lite for a generic aarch64
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[RPi build instructions](build_rpi.md) Compile Tensorflow Lite for a generic aarch64
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board such as Odroid C2, Pine64, NanoPi, and others by following the
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[ARM64 Linux build instructions](linux_aarch64.md) This compiles a static
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[ARM64 Linux build instructions](build_arm64.md) This compiles a static
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library file (`.a`) used to build your app. There are plans for Python bindings
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and a demo app.
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@ -253,7 +253,7 @@ tflite_quantized_model=converter.convert()
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open(“quantized_model.tflite”, “wb”).write(tflite_quantized_model)
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```
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Read the full documentation [here](performance/post_training_quantization.md)
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Read the full documentation [here](../performance/post_training_quantization.md)
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and see a tutorial
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[here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tutorials/post_training_quant.ipynb).
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@ -268,4 +268,4 @@ Another benefit with GPU inference is its power efficiency. GPUs carry out the
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computations in a very efficient and optimized manner, so that they consume less
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power and generate less heat than when the same task is run on CPUs.
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Read the tutorial [here](performance/gpu) and full documentation [here](performance/gpu_advanced).
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Read the tutorial [here](../performance/gpu.md) and full documentation [here](../performance/gpu_advanced.md).
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@ -3,7 +3,7 @@
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The following is an incomplete list of pre-trained models optimized to work with
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TensorFlow Lite.
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To get started choosing a model, visit <a href="./">Models</a>.
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To get started choosing a model, visit <a href="../models">Models</a>.
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Note: The best model for a given application depends on your requirements. For
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example, some applications might benefit from higher accuracy, while others
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@ -13,7 +13,7 @@ models to find the optimal balance between size, performance, and accuracy.
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## Image classification
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For more information about image classification, see
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<a href="image_classification/overview.md">Image classification</a>.
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<a href="../image_classification/overview.md">Image classification</a>.
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### Quantized models
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@ -50,7 +50,7 @@ Graph.
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Note: Performance numbers were benchmarked on Pixel-2 using single thread large
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core. Accuracy numbers were computed using the
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[TFLite accuracy tool](../tools/accuracy/ilsvrc.md).
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[TFLite accuracy tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/accuracy/ilsvrc).
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### Floating point models
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@ -108,7 +108,7 @@ BIG core.
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## Object detection
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For more information about object detection, see
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<a href="object_detection/overview.md">Object detection</a>.
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<a href="../models/object_detection/overview.md">Object detection</a>.
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The object detection model we currently host is
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**coco_ssd_mobilenet_v1_1.0_quant_2018_06_29**.
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@ -119,7 +119,7 @@ model and labels</a>
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## Pose estimation
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For more information about pose estimation, see
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<a href="pose_estimation/overview.md">Pose estimation</a>.
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<a href="../models/pose_estimation/overview.md">Pose estimation</a>.
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The pose estimation model we currently host is
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**multi_person_mobilenet_v1_075_float**.
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@ -130,7 +130,7 @@ model</a>
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## Image segmentation
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For more information about image segmentation, see
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<a href="segmentation/overview.md">Segmentation</a>.
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<a href="../models/segmentation/overview.md">Segmentation</a>.
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The image segmentation model we currently host is **deeplabv3_257_mv_gpu**.
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@ -140,7 +140,7 @@ model</a>
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## Smart reply
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For more information about smart reply, see
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<a href="smart_reply/overview.md">Smart reply</a>.
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<a href="../models/smart_reply/overview.md">Smart reply</a>.
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The smart reply model we currently host is **smartreply_1.0_2017_11_01**.
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@ -118,7 +118,7 @@ TensorFlow Lite provides:
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to all first-party and third-party apps.
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Also see the complete list of
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[TensorFlow Lite's supported models](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/models.md),
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[TensorFlow Lite's supported models](hosted_models.md),
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including the model sizes, performance numbers, and downloadable model files.
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- Quantized versions of the MobileNet model, which runs faster than the
|
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@ -7,7 +7,7 @@
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TensorFlow Lite inference is the process of executing a TensorFlow Lite
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model on-device and extracting meaningful results from it. Inference is the
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final step in using the model on-device in the
|
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[architecture](./index.md#tensorflow_lite_architecture).
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[architecture](index.md#tensorflow_lite_architecture).
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Inference for TensorFlow Lite models is run through an interpreter. This
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document outlines the various APIs for the interpreter along with the
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@ -51,14 +51,14 @@ On Android, TensorFlow Lite inference can be performed using either Java or C++
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APIs. The Java APIs provide convenience and can be used directly within your
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Android Activity classes. The C++ APIs on the other hand may offer more
|
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flexibility and speed, but may require writing JNI wrappers to move data between
|
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Java and C++ layers. You can find an example [here](./android.md).
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Java and C++ layers. You can find an example [here](android.md).
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#### iOS
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TensorFlow Lite provides Swift/Objective C++ APIs for inference on iOS. An
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example can be found [here](./ios.md).
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example can be found [here](ios.md).
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#### Linux
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On Linux platforms such as [Raspberry Pi](./build_rpi.md), TensorFlow Lite C++
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On Linux platforms such as [Raspberry Pi](build_rpi.md), TensorFlow Lite C++
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and Python APIs can be used to run inference.
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@ -72,7 +72,7 @@ should be no surprise that the APIs try to avoid unnecessary copies at the
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expense of convenience. Similarly, consistency with TensorFlow APIs was not an
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explicit goal and some variance is to be expected.
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There is also a [Python API for TensorFlow Lite](./../convert/python_api.md).
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There is also a [Python API for TensorFlow Lite](../convert/python_api.md).
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### Loading a Model
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@ -205,7 +205,7 @@ where each entry in `inputs` corresponds to an input tensor and
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`map_of_indices_to_outputs` maps indices of output tensors to the corresponding
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output data. In both cases the tensor indices should correspond to the values
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given to the
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[TensorFlow Lite Optimized Converter](./../convert/cmdline_examples.md) when the
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[TensorFlow Lite Optimized Converter](../convert/cmdline_examples.md) when the
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model was created. Be aware that the order of tensors in `input` must match the
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order given to the `TensorFlow Lite Optimized Converter`.
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|
||||
@ -79,16 +79,16 @@ Under `Project navigator -> tflite_camera_example -> Targets ->
|
||||
tflite_camera_example -> General` change the bundle identifier by pre-pending
|
||||
your name:
|
||||
|
||||
|
||||
|
||||
|
||||
Plug in your iOS device. Note the app must be executed with a real device with
|
||||
camera. Select the iOS device from the drop-down menu.
|
||||
|
||||
|
||||
|
||||
|
||||
Click the "Run" button to build and run the app
|
||||
|
||||
|
||||
|
||||
|
||||
Note that as mentioned earlier, you must already have a device set up and linked
|
||||
to your Apple Developer account in order to deploy the app on a device.
|
||||
|
||||
@ -9,7 +9,7 @@ Since the set of TensorFlow Lite operations is smaller than TensorFlow's, not
|
||||
every model is convertible. Even for supported operations, very specific usage
|
||||
patterns are sometimes expected, for performance reasons. We expect to expand
|
||||
the set of supported operations in future TensorFlow Lite releases. Additional
|
||||
ops can be included by [using select TensorFlow ops](using_select_tf_ops.md), at
|
||||
ops can be included by [using select TensorFlow ops](ops_select.md), at
|
||||
the cost of binary size.
|
||||
|
||||
The best way to understand how to build a TensorFlow model that can be used with
|
||||
@ -27,7 +27,7 @@ between floating-point and quantized models lies in the way they are converted.
|
||||
Quantized conversion requires dynamic range information for tensors. This
|
||||
requires "fake-quantization" during model training, getting range information
|
||||
via a calibration data set, or doing "on-the-fly" range estimation. See
|
||||
[quantization](performance/model_optimization.md).
|
||||
[quantization](../performance/model_optimization.md).
|
||||
|
||||
## Data Format and Broadcasting
|
||||
|
||||
|
||||
@ -15,7 +15,7 @@ please send feedback about models that work and issues you are facing to
|
||||
tflite@tensorflow.org.
|
||||
|
||||
TensorFlow Lite will continue to have
|
||||
[TensorFlow Lite builtin ops](tf_ops_compatibility.md) optimized for mobile and
|
||||
[TensorFlow Lite builtin ops](ops_compatibility.md) optimized for mobile and
|
||||
embedded devices. However, TensorFlow Lite models can now use a subset of
|
||||
TensorFlow ops when TFLite builtin ops are not sufficient.
|
||||
|
||||
@ -34,7 +34,7 @@ choice. It also discusses some [known limitations](#known-limitations), the
|
||||
|
||||
To convert a TensorFlow model to a TensorFlow Lite model with TensorFlow ops,
|
||||
use the `target_ops` argument in the
|
||||
[TensorFlow Lite converter](https://www.tensorflow.org/lite/convert/). The
|
||||
[TensorFlow Lite converter](../convert/index.md). The
|
||||
following values are valid options for `target_ops`:
|
||||
|
||||
* `TFLITE_BUILTINS` - Converts models using TensorFlow Lite builtin ops.
|
||||
@ -64,7 +64,7 @@ open("converted_model.tflite", "wb").write(tflite_model)
|
||||
```
|
||||
|
||||
The following example shows how to use `target_ops` in the
|
||||
[`tflite_convert`](https://www.tensorflow.org/lite/convert/cmdline_examples)
|
||||
[`tflite_convert`](../convert/cmdline_examples.md)
|
||||
command line tool.
|
||||
|
||||
```
|
||||
@ -97,7 +97,7 @@ includes the necessary library of TensorFlow ops.
|
||||
### Android AAR
|
||||
|
||||
A new Android AAR target with select TensorFlow ops has been added for
|
||||
convenience. Assuming a <a href="./demo_android.md">working TensorFlow Lite
|
||||
convenience. Assuming a <a href="android.md">working TensorFlow Lite
|
||||
build environment</a>, build the Android AAR with select TensorFlow ops as
|
||||
follows:
|
||||
|
||||
|
||||
@ -15,14 +15,14 @@ If you understand image classification, you’re new to TensorFlow Lite, and
|
||||
you’re working with Android or iOS, we recommend following the corresponding
|
||||
tutorial that will walk you through our sample code.
|
||||
|
||||
<a class="button button-primary" href="android">Android</a>
|
||||
<a class="button button-primary" href="ios">iOS</a>
|
||||
<a class="button button-primary" href="android.md">Android</a>
|
||||
<a class="button button-primary" href="ios.md">iOS</a>
|
||||
|
||||
We also provide <a href="example_applications">example applications</a> you can
|
||||
use to get started.
|
||||
|
||||
If you are using a platform other than Android or iOS, or you are already
|
||||
familiar with the <a href="../../apis">TensorFlow Lite APIs</a>, you can
|
||||
familiar with the <a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite APIs</a>, you can
|
||||
download our starter image classification model and the accompanying labels.
|
||||
|
||||
<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip">Download
|
||||
@ -34,7 +34,7 @@ performance, accuracy, and model size. For guidance, see
|
||||
<a href="#choose_a_different_model">Choose a different model</a>.
|
||||
|
||||
If you are using a platform other than Android or iOS, or you are already
|
||||
familiar with the <a href="../../apis.md">TensorFlow Lite APIs</a>, you can
|
||||
familiar with the <a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite APIs</a>, you can
|
||||
download our starter image classification model and the accompanying labels.
|
||||
|
||||
<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip">Download
|
||||
@ -46,7 +46,7 @@ We have example applications for image classification for both Android and iOS.
|
||||
|
||||
<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/android">Android
|
||||
example</a>
|
||||
<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios">iOS
|
||||
<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios.md">iOS
|
||||
example</a>
|
||||
|
||||
The following screenshot shows the Android image classification example:
|
||||
@ -204,8 +204,8 @@ If you want to train a model to recognize new classes, see
|
||||
For the following use cases, you should use a different type of model:
|
||||
|
||||
<ul>
|
||||
<li>Predicting the type and position of one or more objects within an image (see <a href="object_detection">object detection</a>)</li>
|
||||
<li>Predicting the composition of an image, for example subject versus background (see <a href="segmentation">segmentation</a>)</li>
|
||||
<li>Predicting the type and position of one or more objects within an image (see <a href="../object_detection/overview.md">object detection</a>)</li>
|
||||
<li>Predicting the composition of an image, for example subject versus background (see <a href="../segmentation/overview.md">segmentation</a>)</li>
|
||||
</ul>
|
||||
|
||||
Once you have the starter model running on your target device, you can
|
||||
@ -239,7 +239,7 @@ We measure accuracy in terms of how often the model correctly classifies an
|
||||
image. For example, a model with a stated accuracy of 60% can be expected to
|
||||
classify an image correctly an average of 60% of the time.
|
||||
|
||||
Our <a href="../hosted.md">List of hosted models</a> provides Top-1 and Top-5
|
||||
Our <a href="../../guide/hosted_models.md">list of hosted models</a> provides Top-1 and Top-5
|
||||
accuracy statistics. Top-1 refers to how often the correct label appears as the
|
||||
label with the highest probability in the model’s output. Top-5 refers to how
|
||||
often the correct label appears in the top 5 highest probabilities in the
|
||||
@ -258,14 +258,14 @@ Our quantized Mobilenet models’ size ranges from 0.5 to 3.4 Mb.
|
||||
### Architecture
|
||||
|
||||
There are several different architectures of models available on
|
||||
<a href="../hosted.md">List of hosted models</a>, indicated by the model’s name.
|
||||
<a href="../../guide/hosted_models.md">List of hosted models</a>, indicated by the model’s name.
|
||||
For example, you can choose between Mobilenet, Inception, and others.
|
||||
|
||||
The architecture of a model impacts its performance, accuracy, and size. All of
|
||||
our hosted models are trained on the same data, meaning you can use the provided
|
||||
statistics to compare them and choose which is optimal for your application.
|
||||
|
||||
Note: The image classification models we provide accept varying sizes of input. For some models, this is indicated in the filename. For example, the Mobilenet_V1_1.0_224 model accepts an input of 224x224 pixels. <br /><br />All of the models require three color channels per pixel (red, green, and blue). Quantized models require 1 byte per channel, and float models require 4 bytes per channel.<br /><br />Our <a href="android.md">Android</a> and <a href="ios">iOS</a> code samples demonstrate how to process full-sized camera images into the required format for each model.
|
||||
Note: The image classification models we provide accept varying sizes of input. For some models, this is indicated in the filename. For example, the Mobilenet_V1_1.0_224 model accepts an input of 224x224 pixels. <br /><br />All of the models require three color channels per pixel (red, green, and blue). Quantized models require 1 byte per channel, and float models require 4 bytes per channel.<br /><br />Our <a href="android.md">Android</a> and <a href="ios.md">iOS</a> code samples demonstrate how to process full-sized camera images into the required format for each model.
|
||||
|
||||
## Customize model
|
||||
|
||||
|
||||
@ -17,7 +17,7 @@ example</a>
|
||||
example</a>
|
||||
|
||||
If you are using a platform other than Android or iOS, or you are already
|
||||
familiar with the <a href="../apis.md">TensorFlow Lite APIs</a>, you can
|
||||
familiar with the <a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite APIs</a>, you can
|
||||
download our starter object detection model and the accompanying labels.
|
||||
|
||||
<a class="button button-primary" href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
Post-training quantization is a general technique to reduce model size while also
|
||||
providing up to 3x lower latency with little degradation in model accuracy. Post-training
|
||||
quantization quantizes weights from floating point to 8-bits of precision. This technique
|
||||
is enabled as an option in the [TensorFlow Lite converter](../convert):
|
||||
is enabled as an option in the [TensorFlow Lite converter](../convert/index.md):
|
||||
|
||||
```
|
||||
import tensorflow as tf
|
||||
@ -31,7 +31,7 @@ Hybrid ops are available for the most compute-intensive operators in a network:
|
||||
|
||||
Since weights are quantized post training, there could be an accuracy loss, particularly for
|
||||
smaller networks. Pre-trained fully quantized models are provided for specific networks in
|
||||
the [TensorFlow Lite model repository](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/models.md#image-classification-quantized-models){:.external}. It is important to check the accuracy of the quantized model to verify that any degradation
|
||||
the [TensorFlow Lite model repository](../models/). It is important to check the accuracy of the quantized model to verify that any degradation
|
||||
in accuracy is within acceptable limits. There is a tool to evaluate [TensorFlow Lite model accuracy](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tools/accuracy/README.md){:.external}.
|
||||
|
||||
If the accuracy drop is too high, consider using [quantization aware training](https://github.com/tensorflow/tensorflow/tree/r1.13/tensorflow/contrib/quantize){:.external}.
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user