Instructions to use zeromodels/tf_efficientnet_b3_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/tf_efficientnet_b3_in1k with ZeroModels:
# pip install -U zeromodels # ZeroModels is pure Keras 3, so pick a backend: "jax", "torch" or "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" from zeromodels import AutoZModel # AutoZModel reads the repo's model_type and loads the matching class. # For a task head use the matching loader, e.g. AutoZMImageClassify / AutoZMDetect / # AutoZMSemanticSegment / AutoZMTextGenerate (see zeromodels.auto). model = AutoZModel.from_weights("zeromodels/tf_efficientnet_b3_in1k") - Keras
How to use zeromodels/tf_efficientnet_b3_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://zeromodels/tf_efficientnet_b3_in1k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad88be0c75137f4a69e3f3d3ca8cbca050bd2a599486002ed232651222777b40
- Size of remote file:
- 50.3 MB
- SHA256:
- b34418400c3e0f3b292608da1401647196f736403695936e4c2c99a2f47d543c
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