Instructions to use LZSI2026-3/EfficientNetB0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use LZSI2026-3/EfficientNetB0 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://LZSI2026-3/EfficientNetB0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21c2b5c99a72166b906dd1abacf54ac15eeab6190572dc085d0098d47bad8c0b
- Size of remote file:
- 29.8 MB
- SHA256:
- 19b454c25b10958f9afaf5f35d786c0197efe0bd24835b83cff0100b241e5479
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