Hello Image Classification

This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS. To run without installing anything, click the “launch binder” or “Open in Colab” button.

This basic introduction to OpenVINO™ shows how to do inference with an image classification model.

A pre-trained MobileNetV3 model from Open Model Zoo is used in this tutorial. For more information about how OpenVINO IR models are created, refer to the TensorFlow to OpenVINO tutorial.

Table of contents:

• Imports

• Download the Model and data samples

• Select inference device

• Load the Model

• Load an Image

• Do Inference

# Install openvino package
%pip install -q "openvino>=2023.1.0"

Note: you may need to restart the kernel to use updated packages.

Imports

from pathlib import Path

import cv2
import matplotlib.pyplot as plt
import numpy as np
import openvino as ov

# Fetch `notebook_utils` module
import urllib.request
urllib.request.urlretrieve(
    url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
    filename='notebook_utils.py'
)

from notebook_utils import download_file

Download the Model and data samples

base_artifacts_dir = Path('./artifacts').expanduser()

model_name = "v3-small_224_1.0_float"
model_xml_name = f'{model_name}.xml'
model_bin_name = f'{model_name}.bin'

model_xml_path = base_artifacts_dir / model_xml_name

base_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/mobelinet-v3-tf/FP32/'

if not model_xml_path.exists():
    download_file(base_url + model_xml_name, model_xml_name, base_artifacts_dir)
    download_file(base_url + model_bin_name, model_bin_name, base_artifacts_dir)
else:
    print(f'{model_name} already downloaded to {base_artifacts_dir}')

artifacts/v3-small_224_1.0_float.xml:   0%|          | 0.00/294k [00:00<?, ?B/s]

artifacts/v3-small_224_1.0_float.bin:   0%|          | 0.00/4.84M [00:00<?, ?B/s]

Select inference device

select device from dropdown list for running inference using OpenVINO

import ipywidgets as widgets

core = ov.Core()
device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value='AUTO',
    description='Device:',
    disabled=False,
)

device

Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')

Load the Model

core = ov.Core()
model = core.read_model(model=model_xml_path)
compiled_model = core.compile_model(model=model, device_name=device.value)

output_layer = compiled_model.output(0)

Load an Image

# Download the image from the openvino_notebooks storage
image_filename = download_file(
    "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
    directory="data"
)

# The MobileNet model expects images in RGB format.
image = cv2.cvtColor(cv2.imread(filename=str(image_filename)), code=cv2.COLOR_BGR2RGB)

# Resize to MobileNet image shape.
input_image = cv2.resize(src=image, dsize=(224, 224))

# Reshape to model input shape.
input_image = np.expand_dims(input_image, 0)
plt.imshow(image);

data/coco.jpg:   0%|          | 0.00/202k [00:00<?, ?B/s]

Do Inference

result_infer = compiled_model([input_image])[output_layer]
result_index = np.argmax(result_infer)

imagenet_filename = download_file(
    "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/datasets/imagenet/imagenet_2012.txt",
    directory="data"
)

imagenet_classes = imagenet_filename.read_text().splitlines()

data/imagenet_2012.txt:   0%|          | 0.00/30.9k [00:00<?, ?B/s]

# The model description states that for this model, class 0 is a background.
# Therefore, a background must be added at the beginning of imagenet_classes.
imagenet_classes = ['background'] + imagenet_classes

imagenet_classes[result_index]

'n02099267 flat-coated retriever'