TensorFlow Hub has moved to Kaggle Models

Starting November 15th 2023, links to tfhub.dev redirect to their counterparts on Kaggle Models. tensorflow_hub will continue to support downloading models that were initially uploaded to tfhub.dev via e.g. hub.load("https://tfhub.dev/<publisher>/<model>/<version>"). Although no migration or code rewrites are explicitly required, we recommend replacing tfhub.dev links with their Kaggle Models counterparts to improve code health and debuggability. See FAQs here.

As of March 18, 2024, unmigrated model assets (see list below) were deleted and retrieval is no longer possible. These unmigrated model assets include:

• inaturalist/vision/embedder/inaturalist_V2
• nvidia/unet/industrial/class_1
• nvidia/unet/industrial/class_2
• nvidia/unet/industrial/class_3
• nvidia/unet/industrial/class_4
• nvidia/unet/industrial/class_5
• nvidia/unet/industrial/class_6
• nvidia/unet/industrial/class_7
• nvidia/unet/industrial/class_8
• nvidia/unet/industrial/class_9
• nvidia/unet/industrial/class_10
• silero/silero-stt/de
• silero/silero-stt/en
• silero/silero-stt/es
• svampeatlas/vision/classifier/fungi_mobile_V1
• svampeatlas/vision/embedder/fungi_V2

tensorflow_hub

This GitHub repository hosts the tensorflow_hub Python library to download and reuse SavedModels in your TensorFlow program with a minimum amount of code, as well as other associated code and documentation.

Getting Started

• Introduction
• The asset types of tfhub.dev
  • SavedModels for TensorFlow 2 and the Reusable SavedModel interface.
  • Deprecated: Models in TF1 Hub format and their Common Signatures collection.
• Using the library
  • Installation
  • Caching model downloads
  • Migration to TF2
  • Model compatibility for TF1/TF2
  • Common issues
  • Build from source
  • Hosting a module
• Tutorials
  • TF2 Image Retraining
  • TF2 Text Classification
  • Additional TF1 and TF2 examples

Contributing

If you'd like to contribute to TensorFlow Hub, be sure to review the contribution guidelines. To contribute code to the library itself (not examples), you will probably need to build from source.

This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs.

License

Apache License 2.0