Instructions to use zeromodels/nextvit_base_bd_in1k_384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use zeromodels/nextvit_base_bd_in1k_384 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/nextvit_base_bd_in1k_384") - Notebooks
- Google Colab
- Kaggle
Upgrade kf_config.json to v2 (nested config + weight_dtype)
Browse files- kf_config.json +27 -23
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.nextvit",
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"model_class": "NextViTImageClassify",
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"variant": "nextvit_base_bd_in1k_384",
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"weights": "model.weights.h5",
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"model_type": "nextvit",
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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.nextvit",
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"model_class": "NextViTImageClassify",
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"variant": "nextvit_base_bd_in1k_384",
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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": "nextvit",
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"vision_config": {
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"depths": [
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3,
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4,
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20,
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3
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],
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"stem_chs": [
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64,
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32,
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64
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],
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"head_dim": 32,
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"mix_block_ratio": 0.75,
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"sr_ratios": [
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8,
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2,
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1
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],
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"drop_path_rate": 0.1,
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"image_size": 384,
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"num_classes": 1000
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}
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}
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