💻How to turn images Into Vectors

A guide to turning images into Vectors.

Assumed Knowledge: Vectors Target Audience: Python developers, general developers Reading Time: 3 minutes

To help transform data into vectors, we open-sourced a library called VectorHub (you can explore the hub at hub.vctr.ai). For this, you will need to use Python, and you can run all of the below on Colab.

The library can be installed via pip:

$ pip install vectorhub[encoders-image-tfhub]

Once you install via pip, you can then use a model in Python. For example:

from vectorhub.encoders.image.tfhub import BitMedium2Vec
enc = BitMedium2Vec()
image_url = "https://upload.wikimedia.org/wikipedia/commons/8/85/Elon_Musk_Royal_Society_%28crop1%29.jpg"
vector = enc.encode(image_url)

From this - you will have obtained a vector which can now be indexed and stored away for search. If you are interested in reading what is occurring under the hood or to write your own library for this - take a look below.

What is occurring under the hood?

We vectorise an image by firstly reading in an image, which is turned into an array, resized for the model and fed through the model to extract the vector. Note: models are not necessarily trained for the best vectors and representation space and specific models will need to be identified for different use cases. If there is a use case you would like, feel free to message us in our Discord.