TensorFlow Hub is moving to Kaggle Models
Starting November 15th, links to tfhub.dev will redirect to their counterparts on Kaggle Models.
Benefits of Kaggle Models Repository
We’re excited to join the Kaggle community, giving ML developers and learners even more opportunities to experiment and develop ML models in real-world use-cases. Users and developers will benefit from:
- A broader, framework-agnostic model collection
- Comments and feedback from the community
- Better user interface, control over your user profile, and improved model usage statistics
- …and much more!
Accessing Models and their Model Pages
URLs pointing to model pages on tfhub.dev will be redirected to their respective model pages on Kaggle Models (e.g. TensorFlow Hub will redirect to https://www.kaggle.com/models/google/efficientnet-v2/frameworks/tensorFlow2/variations/imagenet1k-b3-classification/versions/2). Model downloads via the tensorflow_hub Python library (e.g. hub.load(“TensorFlow Hub”)) will work automatically by downloading the mirrored models from Kaggle.
Although no migration or code rewrites are explicitly required, we recommend replacing tfhub.dev links with their Kaggle Models counterparts before November 15th to improve code health and debuggability.
How to join Early Access Model Publishing (EAP) on Kaggle Models:
- Email firstname.lastname@example.org and provide the followings to get access to EAP:
- (1) Your Kaggle username
- (2) Your desired organization slug
- (3) A URL to a square-shaped profile image (which is needed for the organization creation)
- Follow documentation instructions to create and publish your model.
- Feel free to raise any questions and get support from Kaggle Discord channel.
Thank you for using tfhub.dev over the years and see you at Kaggle Models!