<jupyter_start><jupyter_text>Model Conversion It converts pre-trained model from any of the popular frameworks to Intermediate Representation (IR) format with the desired precision.<jupyter_code>%load_ext autoreload
%autoreload 2
import os
# Check how to use the model downloader script
!/opt/intel/openvino/deployment_tools/model_optimizer/mo.py --help
INPUT_MODEL_FPATH = "./models/custom_crack_detection/crack_detection.pb"
OUT_DIR = os.path.dirname(INPUT_MODEL_FPATH)
DATA_TYPE = 'FP32'
MODEL_NAME = 'fp32_crack_detection'
!/opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model {INPUT_MODEL_FPATH} \
--output_dir {OUT_DIR} \
--input_shape [1,352,640,3] \
--model_name {MODEL_NAME} \
--data_type {DATA_TYPE} \
--log_level WARNING<jupyter_output>Model Optimizer arguments:
Common parameters:
	- Path to the Input Model: 	/Users/sanchit/Documents/Projects/openvino_opencv/./models/custom_crack_detection/crack_detection.pb
	- Path for generated IR: 	/Users/sanchit/Documents/Projects/openvino_opencv/./models/custom_crack_detection
	- IR output name: 	fp32_crack_detection
	- Log level: 	WARNING
	- Batch: 	Not specified, inherited from the model
	- Input layers: 	Not specified, inherited from the model
	- Output layers: 	Not specified, inherited from the model
	- Input shapes: 	[1,352,640,3]
	- Mean values: 	Not specified
	- Scale values: 	Not specified
	- Scale factor: 	Not specified
	- Precision of IR: 	FP32
	- Enable fusing: 	True
	- Enable grouped convolutions fusing: 	True
	- Move mean values to preprocess section: 	False
	- Reverse input channels: 	False
TensorFlow specific parameters:
	- Input model in text protobuf format: 	False
	- Path to model dump for TensorBoard: 	None
	- List of shared libraries with TensorFlow custom la[...]
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