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Image Classification

In this tutorial, we will explore how to use Hugging Face pipeline, and how to deploy it with Pinferencia as REST API.

Prerequisite

Please visit Dependencies

Download the model and predict

The model will be automatically downloaded.

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from transformers import pipeline
vision_classifier = pipeline(task="image-classification")

vision_classifier(
    images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
)

hfimg

Result:

[{'label': 'lynx, catamount', 'score': 0.4403027892112732},
 {'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
  'score': 0.03433405980467796},
 {'label': 'snow leopard, ounce, Panthera uncia',
  'score': 0.032148055732250214},
 {'label': 'Egyptian cat', 'score': 0.02353910356760025},
 {'label': 'tiger cat', 'score': 0.023034192621707916}]

Amazingly easy! Now let's try:

Deploy the model

Without deployment, how could a machine learning tutorial be complete?

First, let's install Pinferencia.

pip install "pinferencia[streamlit]"
Now let's create an app.py file with the codes:

app.py
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from transformers import pipeline

from pinferencia import Server, task

vision_classifier = pipeline(task="image-classification")


def classify(data: str) -> list:
    return vision_classifier(images=data)


service = Server()
service.register(
    model_name="vision", model=classify, metadata={"task": task.TEXT_TO_TEXT}
)

Easy, right?

Predict

curl --location --request POST 'http://127.0.0.1:8000/v1/models/vision/predict' \
    --header 'Content-Type: application/json' \
    --data-raw '{
        "data": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
    }'

Result: 

Prediction: [
    {'score': 0.433499813079834, 'label': 'lynx, catamount'},
    {'score': 0.03479616343975067, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'},
    {'score': 0.032401904463768005, 'label': 'snow leopard, ounce, Panthera uncia'},
    {'score': 0.023944756016135216, 'label': 'Egyptian cat'},
    {'score': 0.022889181971549988, 'label': 'tiger cat'}
]

test.py
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import requests

response = requests.post(
    url="http://localhost:8000/v1/models/vision/predict",
    json={
        "data": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"  # noqa
    },
)
print("Prediction:", response.json()["data"])

Run python test.py and result:

Prediction: [
    {'score': 0.433499813079834, 'label': 'lynx, catamount'},
    {'score': 0.03479616343975067, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'},
    {'score': 0.032401904463768005, 'label': 'snow leopard, ounce, Panthera uncia'},
    {'score': 0.023944756016135216, 'label': 'Egyptian cat'},
    {'score': 0.022889181971549988, 'label': 'tiger cat'}
]

Even cooler, go to http://127.0.0.1:8501, and you will have an interactive ui.

You can send predict request just there!

Improve it

However, using the url of the image to predict sometimes is not always convenient.

Let's modify the app.py a little bit to accept Base64 Encoded String as the input.

app.py
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import base64
from io import BytesIO

from PIL import Image
from transformers import pipeline

from pinferencia import Server, task

vision_classifier = pipeline(task="image-classification")


def classify(images: list) -> list:
    """Image Classification

    Args:
        images (list): list of base64 encoded image strings

    Returns:
        list: list of classification results
    """
    input_images = [Image.open(BytesIO(base64.b64decode(img))) for img in images]
    return vision_classifier(images=input_images)


service = Server()
service.register(
    model_name="vision",
    model=classify,
    metadata={"task": task.IMAGE_CLASSIFICATION},
)

Predict Again

Open http://127.0.0.1:8501, and the template Image Classification will be selected automatically.

UI

curl --location --request POST 'http://127.0.0.1:8000/v1/models/vision/predict' \
--header 'Content-Type: application/json' \
--data-raw '{
    "data": "..."
}'

Result: 

Prediction: [
    {'score': 0.433499813079834, 'label': 'lynx, catamount'},
    {'score': 0.03479616343975067, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'},
    {'score': 0.032401904463768005, 'label': 'snow leopard, ounce, Panthera uncia'},
    {'score': 0.023944756016135216, 'label': 'Egyptian cat'},
    {'score': 0.022889181971549988, 'label': 'tiger cat'}
]

test.py
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import requests

response = requests.post(
    url="http://localhost:8000/v1/models/vision/predict",
    json={
        "data": "..."  # noqa
    },
)
print("Prediction:", response.json()["data"])

Run python test.py and result:

Prediction: [
    {'score': 0.433499813079834, 'label': 'lynx, catamount'},
    {'score': 0.03479616343975067, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'},
    {'score': 0.032401904463768005, 'label': 'snow leopard, ounce, Panthera uncia'},
    {'score': 0.023944756016135216, 'label': 'Egyptian cat'},
    {'score': 0.022889181971549988, 'label': 'tiger cat'}
]