Instructions to use zeromodels/tf_efficientnet_lite0_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/tf_efficientnet_lite0_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_lite0_in1k") - Keras
How to use zeromodels/tf_efficientnet_lite0_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_lite0_in1k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8f520fa74dec55e4a51683f3383bcffd6e24be38e91be8fc87bd492bcad2096
- Size of remote file:
- 19.2 MB
- SHA256:
- 3ad6ef6180fd722066fa502184f17611c4798a79fcfda9990cac462f5ad628a9
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