TensorFlow Hub ❤️ Kaggle

Posted by Luiz GUStavo Martins, Google AI Developer Advocate

We’re excited to announce our new integration with Kaggle Models, a recently launched pre-trained model platform. All 2,300+ TensorFlow models published on TFHub by Google, DeepMind, and more are now discoverable and usable on Kaggle Models, with documentation and sample code.

Why Kaggle?

Kaggle is a global community of over 12 million machine learners who test their knowledge in competitions and share machine learning resources, including over 200,000 public datasets. Over the past 10+ years, Kaggle’s competitions have become a proving ground for what works well and what doesn’t across a multitude of ML use cases. This is why Kaggle recently launched its open model hub, Kaggle Models, to better enable the ML community to stress test and validate models publicly and at scale.

Hosting TensorFlow models on Kaggle makes them more easily accessible to the broader ML community, democratizing model building and advancement. We can’t wait to see what solutions come from this partnership.

How to Get Started

A great place to check out the new integration is with the live Kaggle competition called BirdCLEF 2023 using the recently published Bird Vocalization Classifier model. Participants are challenged to build a model that identifies bird species by sound. Bird populations around the world are falling alarmingly, with approximately 48% of existing species experiencing population declines. The results of this competition contribute to scaling the critical work of bird species monitoring that allows researchers to better evaluate whether interventions are working.

The Bird Vocalization Classifier model was just open-sourced by the Google Research team on TFHub (and subsequently Kaggle Models 🙌). It’s a global bird embedding and classification model that can identify more than 10k bird species’ vocalizations, and also creates embedding vectors that can be used for other tasks.

To try the model on Kaggle:
  1. Navigate to the model here.
  2. Click the “New Notebook” button, which will open a Kaggle Notebooks editor.
  3. Click the “Copy Code” button on the right-hand side of the editor, which will copy sample code that loads the model using the TensorFlow Hub library.
  4. Paste the code into the notebook’s cell, and you’re ready to go!
  5. Click the “Add Model” button at the bottom. This will attach the model to your notebook.
Moving image showing the user's experience of the Bird Vocalization Classifier Model on Kaggle

The snippet imports TFHub library and loads the newly published Bird Vocalization Classifier model. To find more information about this model, you can check its documentation and even play with a full example that demonstrates how to use the model in the competition here.

import tensorflow_hub as hub keras_layer = hub.KerasLayer('https://kaggle.com/models/google/bird-vocalization-classifier/frameworks/TensorFlow2/variations/bird-vocalization-classifier/versions/1')

For more information on Kaggle Models including its current feature set and future roadmap, check the official announcement here. We look forward to seeing what you build as a result of this integration!

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