page_type: reference description: ObjectDetector class for inference and exporting to tflite.

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tflite_model_maker.object_detector.ObjectDetector

<!-- Insert buttons and diff --> <table class="tfo-notebook-buttons tfo-api nocontent" align="left"> <td> <a target="_blank" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/object_detector.py#L37-L264"> </a> </td> </table>

ObjectDetector class for inference and exporting to tflite.

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>tflite_model_maker.object_detector.ObjectDetector( model_spec: <a href="../../tflite_model_maker/object_detector/EfficientDetSpec"><code>tflite_model_maker.object_detector.EfficientDetSpec</code></a>, label_map: Dict[int, str], representative_data: Optional[<a href="../../tflite_model_maker/object_detector/DataLoader"><code>tflite_model_maker.object_detector.DataLoader</code></a>] = None ) -> None </code></pre> <!-- Placeholder for "Used in" --> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Args</h2></th></tr> <tr> <td> `model_spec`<a id="model_spec"></a> </td> <td> Specification for the model. </td> </tr><tr> <td> `label_map`<a id="label_map"></a> </td> <td> Dict, map label integer ids to string label names such as {1: 'person', 2: 'notperson'}. 0 is the reserved key for `background` and doesn't need to be included in `label_map`. Label names can't be duplicated. </td> </tr><tr> <td> `representative_data`<a id="representative_data"></a> </td> <td> Representative dataset for full integer quantization. Used when converting the keras model to the TFLite model with full interger quantization. </td> </tr> </table>

Methods

<h3 id="create"><code>create</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/object_detector.py#L219-L264">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>@classmethod</code> <code>create( train_data: <a href="../../tflite_model_maker/object_detector/DataLoader"><code>tflite_model_maker.object_detector.DataLoader</code></a>, model_spec: <a href="../../tflite_model_maker/object_detector/EfficientDetSpec"><code>tflite_model_maker.object_detector.EfficientDetSpec</code></a>, validation_data: Optional[<a href="../../tflite_model_maker/object_detector/DataLoader"><code>tflite_model_maker.object_detector.DataLoader</code></a>] = None, epochs: Optional[<a href="../../tflite_model_maker/object_detector/DataLoader"><code>tflite_model_maker.object_detector.DataLoader</code></a>] = None, batch_size: Optional[int] = None, train_whole_model: bool = False, do_train: bool = True ) -> T </code></pre>

Loads data and train the model for object detection.

<!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `train_data` </td> <td> Training data. </td> </tr><tr> <td> `model_spec` </td> <td> Specification for the model. </td> </tr><tr> <td> `validation_data` </td> <td> Validation data. If None, skips validation process. </td> </tr><tr> <td> `epochs` </td> <td> Number of epochs for training. </td> </tr><tr> <td> `batch_size` </td> <td> Batch size for training. </td> </tr><tr> <td> `train_whole_model` </td> <td> Boolean, False by default. If true, train the whole model. Otherwise, only train the layers that are not match `model_spec.config.var_freeze_expr`. </td> </tr><tr> <td> `do_train` </td> <td> Whether to run training. </td> </tr> </table> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Returns</th></tr> <tr class="alt"> <td colspan="2"> An instance based on ObjectDetector. </td> </tr> </table> <h3 id="create_model"><code>create_model</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/object_detector.py#L73-L75">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>create_model() -> tf.keras.Model </code></pre> <h3 id="create_serving_model"><code>create_serving_model</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/custom_model.py#L170-L176">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>create_serving_model() </code></pre>

Returns the underlining Keras model for serving.

<h3 id="evaluate"><code>evaluate</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/object_detector.py#L127-L148">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>evaluate( data: <a href="../../tflite_model_maker/object_detector/DataLoader"><code>tflite_model_maker.object_detector.DataLoader</code></a>, batch_size: Optional[int] = None ) -> Dict[str, float] </code></pre>

Evaluates the model.

<h3 id="evaluate_tflite"><code>evaluate_tflite</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/object_detector.py#L150-L156">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>evaluate_tflite( tflite_filepath: str, data: <a href="../../tflite_model_maker/object_detector/DataLoader"><code>tflite_model_maker.object_detector.DataLoader</code></a> ) -> Dict[str, float] </code></pre>

Evaluate the TFLite model.

<h3 id="export"><code>export</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/custom_model.py#L95-L168">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>export( export_dir, tflite_filename=&#x27;model.tflite&#x27;, label_filename=&#x27;labels.txt&#x27;, vocab_filename=&#x27;vocab.txt&#x27;, saved_model_filename=&#x27;saved_model&#x27;, tfjs_folder_name=&#x27;tfjs&#x27;, export_format=None, **kwargs ) </code></pre>

Converts the retrained model based on export_format.

<!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `export_dir` </td> <td> The directory to save exported files. </td> </tr><tr> <td> `tflite_filename` </td> <td> File name to save tflite model. The full export path is {export_dir}/{tflite_filename}. </td> </tr><tr> <td> `label_filename` </td> <td> File name to save labels. The full export path is {export_dir}/{label_filename}. </td> </tr><tr> <td> `vocab_filename` </td> <td> File name to save vocabulary. The full export path is {export_dir}/{vocab_filename}. </td> </tr><tr> <td> `saved_model_filename` </td> <td> Path to SavedModel or H5 file to save the model. The full export path is {export_dir}/{saved_model_filename}/{saved_model.pb|assets|variables}. </td> </tr><tr> <td> `tfjs_folder_name` </td> <td> Folder name to save tfjs model. The full export path is {export_dir}/{tfjs_folder_name}. </td> </tr><tr> <td> `export_format` </td> <td> List of export format that could be saved_model, tflite, label, vocab. </td> </tr><tr> <td> `**kwargs` </td> <td> Other parameters like `quantized_config` for TFLITE model. </td> </tr> </table> <h3 id="summary"><code>summary</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/custom_model.py#L65-L66">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>summary() </code></pre> <h3 id="train"><code>train</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/object_detector.py#L92-L125">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>train( train_data: <a href="../../tflite_model_maker/object_detector/DataLoader"><code>tflite_model_maker.object_detector.DataLoader</code></a>, validation_data: Optional[<a href="../../tflite_model_maker/object_detector/DataLoader"><code>tflite_model_maker.object_detector.DataLoader</code></a>] = None, epochs: Optional[int] = None, batch_size: Optional[int] = None ) -> tf.keras.Model </code></pre>

Feeds the training data for training.

<!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Class Variables</h2></th></tr> <tr> <td> ALLOWED_EXPORT_FORMAT<a id="ALLOWED_EXPORT_FORMAT"></a> </td> <td> `(<ExportFormat.TFLITE: 'TFLITE'>, <ExportFormat.SAVED_MODEL: 'SAVED_MODEL'>, <ExportFormat.LABEL: 'LABEL'>)` </td> </tr><tr> <td> DEFAULT_EXPORT_FORMAT<a id="DEFAULT_EXPORT_FORMAT"></a> </td> <td> `<ExportFormat.TFLITE: 'TFLITE'>` </td> </tr> </table>