page_type: reference description: Creates EfficientDet-Lite0 model spec. See also: <a href="../../tflite_model_maker/object_detector/EfficientDetSpec"><code>tflite_model_maker.object_detector.EfficientDetSpec</code></a>.

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tflite_model_maker.object_detector.EfficientDetLite0Spec

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Creates EfficientDet-Lite0 model spec. See also: <a href="../../tflite_model_maker/object_detector/EfficientDetSpec"><code>tflite_model_maker.object_detector.EfficientDetSpec</code></a>.

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>tflite_model_maker.object_detector.EfficientDetLite0Spec( *, model_name=&#x27;efficientdet-lite0&#x27;, uri=&#x27;https://tfhub.dev/tensorflow/efficientdet/lite0/feature-vector/1&#x27;, hparams=&#x27;&#x27;, model_dir=None, epochs=50, batch_size=64, steps_per_execution=1, moving_average_decay=0, var_freeze_expr=&#x27;(efficientnet|fpn_cells|resample_p6)&#x27;, tflite_max_detections=25, strategy=None, tpu=None, gcp_project=None, tpu_zone=None, use_xla=False, profile=False, debug=False, tf_random_seed=111111, verbose=0 ) </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_name`<a id="model_name"></a> </td> <td> Model name. </td> </tr><tr> <td> `uri`<a id="uri"></a> </td> <td> TF-Hub path/url to EfficientDet module. </td> </tr><tr> <td> `hparams`<a id="hparams"></a> </td> <td> Hyperparameters used to overwrite default configuration. Can be

1. Dict, contains parameter names and values; 2) String, Comma separated k=v pairs of hyperparameters; 3) String, yaml filename which's a module containing attributes to use as hyperparameters.

</td> </tr><tr> <td> `model_dir`<a id="model_dir"></a> </td> <td> The location to save the model checkpoint files. </td> </tr><tr> <td> `epochs`<a id="epochs"></a> </td> <td> Default training epochs. </td> </tr><tr> <td> `batch_size`<a id="batch_size"></a> </td> <td> Training & Evaluation batch size. </td> </tr><tr> <td> `steps_per_execution`<a id="steps_per_execution"></a> </td> <td> Number of steps per training execution. </td> </tr><tr> <td> `moving_average_decay`<a id="moving_average_decay"></a> </td> <td> Float. The decay to use for maintaining moving averages of the trained parameters. </td> </tr><tr> <td> `var_freeze_expr`<a id="var_freeze_expr"></a> </td> <td> Expression to freeze variables. </td> </tr><tr> <td> `tflite_max_detections`<a id="tflite_max_detections"></a> </td> <td> The max number of output detections in the TFLite model. </td> </tr><tr> <td> `strategy`<a id="strategy"></a> </td> <td> A string specifying which distribution strategy to use. Accepted values are 'tpu', 'gpus', None. tpu' means to use TPUStrategy. 'gpus' mean to use MirroredStrategy for multi-gpus. If None, use TF default with OneDeviceStrategy. </td> </tr><tr> <td> `tpu`<a id="tpu"></a> </td> <td> The Cloud TPU to use for training. This should be either the name used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url. </td> </tr><tr> <td> `gcp_project`<a id="gcp_project"></a> </td> <td> Project name for the Cloud TPU-enabled project. If not specified, we will attempt to automatically detect the GCE project from metadata. </td> </tr><tr> <td> `tpu_zone`<a id="tpu_zone"></a> </td> <td> GCE zone where the Cloud TPU is located in. If not specified, we will attempt to automatically detect the GCE project from metadata. </td> </tr><tr> <td> `use_xla`<a id="use_xla"></a> </td> <td> Use XLA even if strategy is not tpu. If strategy is tpu, always use XLA, and this flag has no effect. </td> </tr><tr> <td> `profile`<a id="profile"></a> </td> <td> Enable profile mode. </td> </tr><tr> <td> `debug`<a id="debug"></a> </td> <td> Enable debug mode. </td> </tr><tr> <td> `tf_random_seed`<a id="tf_random_seed"></a> </td> <td> Fixed random seed for deterministic execution across runs for debugging. </td> </tr><tr> <td> `verbose`<a id="verbose"></a> </td> <td> verbosity mode for <a href="https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint"><code>tf.keras.callbacks.ModelCheckpoint</code></a>, 0 or 1. </td> </tr> </table>