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- # Exporting a trained model for inference
-
- After your model has been trained, you should export it to a Tensorflow
- graph proto. A checkpoint will typically consist of three files:
-
- * model.ckpt-${CHECKPOINT_NUMBER}.data-00000-of-00001
- * model.ckpt-${CHECKPOINT_NUMBER}.index
- * model.ckpt-${CHECKPOINT_NUMBER}.meta
-
- After you've identified a candidate checkpoint to export, run the following
- command from tensorflow/models/research:
-
- ``` bash
- # From tensorflow/models/research/
- INPUT_TYPE=image_tensor
- PIPELINE_CONFIG_PATH={path to pipeline config file}
- TRAINED_CKPT_PREFIX={path to model.ckpt}
- EXPORT_DIR={path to folder that will be used for export}
- python object_detection/export_inference_graph.py \
- --input_type=${INPUT_TYPE} \
- --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
- --trained_checkpoint_prefix=${TRAINED_CKPT_PREFIX} \
- --output_directory=${EXPORT_DIR}
- ```
-
- NOTE: We are configuring our exported model to ingest 4-D image tensors. We can
- also configure the exported model to take encoded images or serialized
- `tf.Example`s.
-
- After export, you should see the directory ${EXPORT_DIR} containing the following:
-
- * saved_model/, a directory containing the saved model format of the exported model
- * frozen_inference_graph.pb, the frozen graph format of the exported model
- * model.ckpt.*, the model checkpoints used for exporting
- * checkpoint, a file specifying to restore included checkpoint files
- * pipeline.config, pipeline config file for the exported model
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