Hello Image Classification#

This Jupyter notebook can be launched on-line, opening an interactive environment in a browser window. You can also make a local installation. Choose one of the following options:

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

Installation Instructions#

This is a self-contained example that relies solely on its own code.

We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to Installation Guide.

# Install required packages
%pip install -q "openvino>=2023.1.0" opencv-python tqdm "matplotlib>=3.4"

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 requests

r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
)

open("notebook_utils.py", "w").write(r.text)

from notebook_utils import download_file, device_widget

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}")

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

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

device = device_widget()

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);

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../_images/hello-world-with-output_11_1.png

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()

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'