Instructions to use zeromodels/mobilevit_s_cvnets_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasFormers
How to use zeromodels/mobilevit_s_cvnets_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/mobilevit_s_cvnets_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/mobilevit_s_cvnets_in1k") - Notebooks
- Google Colab
- Kaggle
Upgrade kf_config.json to v2 (nested config + weight_dtype)
Browse files- kf_config.json +38 -26
kf_config.json
CHANGED
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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.mobilevit",
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"model_class": "MobileViTImageClassify",
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"variant": "mobilevit_s_cvnets_in1k",
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"weights": "model.weights.h5",
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"model_type": "mobilevit",
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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.mobilevit",
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"model_class": "MobileViTImageClassify",
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"variant": "mobilevit_s_cvnets_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": "mobilevit",
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"vision_config": {
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"initial_dims": 16,
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"head_dims": 640,
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"block_dims": [
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],
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"expansion_ratio": [
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4.0,
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4.0,
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4.0
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],
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"attention_dims": [
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],
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"image_size": 256,
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"num_classes": 1000,
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"output_stride": 32,
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"atrous_rates": [
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],
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"aspp_out_channels": 256,
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"aspp_dropout_prob": 0.1
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}
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}
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