Instructions to use keras/clip_vit_base_patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/clip_vit_base_patch32 with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/clip_vit_base_patch32") - Keras
How to use keras/clip_vit_base_patch32 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/clip_vit_base_patch32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb31ac912683b2fa274b685f9762c474c71de7b5146a688d736ea21f0fdff517
- Size of remote file:
- 606 MB
- SHA256:
- 15ad808f9a96d9b194ed60bebac6d45c4ad6209c4f06025662f4e9e9d96ffd77
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