Instructions to use zeromodels/tf_efficientnetv2_s_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasFormers
How to use zeromodels/tf_efficientnetv2_s_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 zeromodels/tf_efficientnetv2_s_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_efficientnetv2_s_in1k") - Notebooks
- Google Colab
- Kaggle
Upgrade kf_config.json to v2 (nested config + weight_dtype)
Browse files- kf_config.json +12 -8
kf_config.json
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{
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"library_name": "kerasformers",
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"kerasformers_version": "1.
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"model_module": "kerasformers.models.efficientnetv2",
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"model_class": "EfficientNetV2ImageClassify",
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"variant": "tf_efficientnetv2_s_in1k",
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"weights": "model.weights.h5",
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"model_type": "efficientnetv2",
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"
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}
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{
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"library_name": "kerasformers",
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"kerasformers_version": "1.2.1",
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"model_module": "kerasformers.models.efficientnetv2",
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"model_class": "EfficientNetV2ImageClassify",
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"variant": "tf_efficientnetv2_s_in1k",
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"weights": "model.weights.h5",
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"schema_version": 2,
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"weight_dtype": "float32",
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"model_type": "efficientnetv2",
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"vision_config": {
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"width_coefficient": 1.0,
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"depth_coefficient": 1.0,
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"default_size": 300,
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"block_arch": "EfficientNetV2S",
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"head_filters": 1280,
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"image_size": 300,
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"num_classes": 1000
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}
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}
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