Enabling Data Scientists and Developers with Free and Open Source AI

Announcing the Third Batch of New Models in the IBM Developer Model Asset eXchange

Maureen McElaney
Sep 21, 2018 · 3 min read

IBM’s Model Asset eXchange (MAX) is a one-stop place for developers to find and use free and open source deep learning models. Since the first release of MAX in early 2018, our team of developers at IBM’s Center for Open-Source Data & AI Technologies have enabled many data scientists and AI developers to easily discover, rate, train, and deploy machine learning and deep learning models in their AI applications. To illustrate the usefulness of our Model Asset eXchange we’d like to highlight some new assets and code patterns (or code samples) that we just launched.

Deep Learning Models for Everyone: http://ibm.biz/model-exchange

These new model assets cover Machine Learning and Deep Learning domains including images, audio, text, and time series:

  • Facial Age Estimator — This model estimates biological ages of people given an image. The model first detects and locates faces in the image and then generates a predicted age for each face. The output is the estimated ages, together with the location in the image, of the detected faces.
    You could use this model to automate analysis of visitor traffic patterns within a shopping mall, by various demographics including age.
  • Facial Recognizer — This model recognizes faces in an image and automatically extracts a set of low-level features that represent each face in the form of an embedding vector. These embeddings can be used as input for other machine learning tasks, including classification, clustering, verification, or similarity search.
    You could use this model to generate automated security verification against a database of faces from images and videos.
  • Weather Forecaster — This model forecasts near-term weather variables (such as temperature, pressure, or wind speed), given recent historical data.
    You could use this model to predict local weather conditions to enable more focused and efficient targeting of retail offers to customers.
  • Name Generator — This model can be trained on a list of names. Once trained, this model can score and suggest names based on the data set it is trained on. As an example we have included the Kaggle US Baby Names data set to train the model on.
    You could use this model to generate names or find the country of origin of a name.
  • Audio Sample Generator — This model generates new audio clips that are similar to existing audio clips that the model was trained on. The model can generate short samples of six speech commands (up, down, left, right, stop, go), as well as lo-fi instrumental music.
    You could use this model to generate data to increase the robustness of Natural Language Processing (NLP) and Audio models and increase privacy of the data.

In addition to the new model assets, we also enhanced many things about our existing MAX models. Some highlights of some things you can now find with our MAX models:

  • All pre-trained models are easily deployable with pre-built DockerHub images.
  • All pre-trained models are deployable on a Kubernetes cluster.

We’ve also published two new demo web applications for Image Caption Generator and Object Detector models to help you get started. More are in the works.

Visit the IBM’s Model Asset eXchange (MAX) site and browse through these models and enhancements. We hope you can find something that is right for your AI development use cases. I welcome your comments and suggestions that will help us improve our offerings and better serve you!


Things we made with data at IBM’s Center for Open Source Data and AI Technologies.

Maureen McElaney

Written by

dev advocate at @IBM CODAIT. co-organizer at @vtcodecamp. board of @vttechalliance. content here is mine. Website: http://mcelaney.me/


Things we made with data at IBM’s Center for Open Source Data and AI Technologies.

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