Sebastian Golebiewskiandbstankix
6761a29af5
* update-notebooks * Update docs/nbdoc/nbdoc.py Co-authored-by: bstankix <bartoszx.stankiewicz@intel.com> * Update docs/nbdoc/nbdoc.py Co-authored-by: bstankix <bartoszx.stankiewicz@intel.com> --------- Co-authored-by: bstankix <bartoszx.stankiewicz@intel.com>
152 lines
3.6 KiB
ReStructuredText
152 lines
3.6 KiB
ReStructuredText
Hello Image Classification
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==========================
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This basic introduction to OpenVINO™ shows how to do inference with an
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image classification model.
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A pre-trained `MobileNetV3
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model <https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v3_small_1_0_224_tf.html>`__
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from `Open Model
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Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__ is used in
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this tutorial. For more information about how OpenVINO IR models are
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created, refer to the `TensorFlow to
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OpenVINO <101-tensorflow-classification-to-openvino-with-output.html>`__
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tutorial.
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Imports
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-------
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.. code:: ipython3
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from pathlib import Path
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import sys
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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from openvino.runtime import Core
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sys.path.append("../utils")
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from notebook_utils import download_file
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Download the Model and data samples
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-----------------------------------
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.. code:: ipython3
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base_artifacts_dir = Path('./artifacts').expanduser()
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model_name = "v3-small_224_1.0_float"
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model_xml_name = f'{model_name}.xml'
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model_bin_name = f'{model_name}.bin'
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model_xml_path = base_artifacts_dir / model_xml_name
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base_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/mobelinet-v3-tf/FP32/'
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if not model_xml_path.exists():
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download_file(base_url + model_xml_name, model_xml_name, base_artifacts_dir)
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download_file(base_url + model_bin_name, model_bin_name, base_artifacts_dir)
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else:
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print(f'{model_name} already downloaded to {base_artifacts_dir}')
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.. parsed-literal::
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artifacts/v3-small_224_1.0_float.xml: 0%| | 0.00/294k [00:00<?, ?B/s]
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.. parsed-literal::
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artifacts/v3-small_224_1.0_float.bin: 0%| | 0.00/4.84M [00:00<?, ?B/s]
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Select inference device
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-----------------------
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select device from dropdown list for running inference using OpenVINO
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.. code:: ipython3
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import ipywidgets as widgets
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core = Core()
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device = widgets.Dropdown(
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options=core.available_devices + ["AUTO"],
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value='AUTO',
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description='Device:',
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disabled=False,
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)
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device
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.. parsed-literal::
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Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
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Load the Model
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--------------
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.. code:: ipython3
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core = Core()
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model = core.read_model(model=model_xml_path)
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compiled_model = core.compile_model(model=model, device_name=device.value)
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output_layer = compiled_model.output(0)
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Load an Image
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-------------
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.. code:: ipython3
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# The MobileNet model expects images in RGB format.
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image = cv2.cvtColor(cv2.imread(filename="../data/image/coco.jpg"), code=cv2.COLOR_BGR2RGB)
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# Resize to MobileNet image shape.
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input_image = cv2.resize(src=image, dsize=(224, 224))
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# Reshape to model input shape.
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input_image = np.expand_dims(input_image, 0)
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plt.imshow(image);
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.. image:: 001-hello-world-with-output_files/001-hello-world-with-output_10_0.png
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Do Inference
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------------
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.. code:: ipython3
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result_infer = compiled_model([input_image])[output_layer]
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result_index = np.argmax(result_infer)
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.. code:: ipython3
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# Convert the inference result to a class name.
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imagenet_classes = open("../data/datasets/imagenet/imagenet_2012.txt").read().splitlines()
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# The model description states that for this model, class 0 is a background.
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# Therefore, a background must be added at the beginning of imagenet_classes.
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imagenet_classes = ['background'] + imagenet_classes
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imagenet_classes[result_index]
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.. parsed-literal::
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'n02099267 flat-coated retriever'
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