Image Visualization with Kangas

Applying built-in functions from Kangas UI to Hugging Face DataGrids

According to its website, Hugging Face is a “platform where Users can build, benchmark, share, version and deploy Repositories, which may include Models, Datasets and Machine Learning Applications.”

In order to have access to Hugging Face Datagrids, defined as “Datasets” in their platform, first we will need to install the datasets library in our Python environment, if using conda the recommendation is to create an isolated environment where you will need to install kangas and the datasets library:

and

Then we will continue performing all the analysis in a Jupyter Notebook, where we have previously activated the environment, in my particular case I have created a conda env with the name flask-app with Python3.9 installed.

In order to start exploring the DataGrid with Kangas we’ll import some basic packages that will load the dataset from Hugging face and put Kangas into action:

After that, we can proceed to load the dataset in the notebook by passing some parameters to the function, such as split:

split (Split or str) — Which split of the data to load. If None, will return a dict with all splits (typically datasets.Split.TRAIN and datasets.Split.TEST)

You can also look for all the parameters that take the datasets library here:

Once downloaded in your .cache folder, you can go ahead and start working with your dataset in the notebook with the info() function:

When we have explored the elements that comprise the DataGrid, we can save it. For the sake of simplicity, I will leave it saved in my Temp folder, then I will explore the schema of my DataGrid.

Once the DataGrid is saved, we can start visualizing it in the Kangas server and we have to go to the directory where the DataGrid was saved and start the server from there:

Going to your browser by typing the URL where the server is executed, there you have your DataGrid, and you can start applying filters, sorting and grouping by columns:

In this article we have learned how to load datasets that we can use to start an analysis from scratch and select from a huge public repo of models and datasets oriented to Computer Vision, NLP and Audio recognition. We have also explored some other features of the Kangas API for DataGrids analysis and classification.

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