Visual AI predictions¶

There are various methods for making predictions from image models:

Base64 encoding format¶

If your training dataset consists of a ZIP archive with one or more image files, the prediction dataset needs to be converted to a different format so that it is fully contained in a single CSV file.

Sample scripts¶

See the links below for help with visual data conversion:

• Tutorial: Getting Predictions for Visual AI Projects via API Calls
• DataRobot Python package: Preparing data for predictions using the DataRobot library
• Script: Comprehensive data prep script

The following shows sample usage:

python visualai_data_prep.py pred_dataset.zip pred_dataset.csv image

Where:

• visualai_data_prep.py is the comprehensive Data Prep script used for making conversions to base64 format.
• pred_dataset.zip is the input dataset (ZIP of images).
• pred_dataset.csv is the output, which can be used via prediction API.
• image is an image column name.

Deep dive¶

To convert a set of image files into a single CSV file, each image must be converted to base64 text. This format allows DataRobot to embed images as a regular text column in the CSV. Encoding binary image data into base64 is a simple operation, present in all programming languages.

Here is an example in Python:

import base64
import pandas as pd
from io import BytesIO
from PIL import Image


def image_to_base64(image: Image) -> str:
    img_bytes = BytesIO()
    image.save(img_bytes, 'jpeg', quality=90)
    image_base64 = base64.b64encode(img_bytes.getvalue()).decode('utf-8')
    return image_base64


# let's build a CSV with a single row that contains an image
# the same general approach works if you have multiple image rows or columns
image = Image.open('cat.jpg')
image_base64 = image_to_base64(image)

df = pd.DataFrame({'animal_image': [image_base64]})
df.to_csv('prediction_dataset.csv' index=False)
print(df)