Instructions to use kerasformers/tf_efficientnet_b2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasFormers
How to use kerasformers/tf_efficientnet_b2_in1k with KerasFormers:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Keras
How to use kerasformers/tf_efficientnet_b2_in1k with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://kerasformers/tf_efficientnet_b2_in1k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9043f27a89ebc86c6fd0873d5d4d5d70a55d2d8e1890b63bd94b068a8e91d75
- Size of remote file:
- 37.6 MB
- SHA256:
- 4167f28c94819006e1789e19d857d73faf8c08209672671ec3e1898bb28bb1a7
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