Instructions to use keras/vit_large_patch32_384_imagenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/vit_large_patch32_384_imagenet with KerasHub:
import keras_hub import keras # Load ImageClassifier model image_classifier = keras_hub.models.ImageClassifier.from_preset( "hf://keras/vit_large_patch32_384_imagenet", num_classes=2, ) # Fine-tune image_classifier.fit( x=keras.random.randint((32, 64, 64, 3), 0, 256), y=keras.random.randint((32, 1), 0, 2), ) # Classify image image_classifier.predict(keras.random.randint((1, 64, 64, 3), 0, 256))import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/vit_large_patch32_384_imagenet") - Keras
How to use keras/vit_large_patch32_384_imagenet 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/vit_large_patch32_384_imagenet") - Notebooks
- Google Colab
- Kaggle
| { | |
| "keras_version": "3.14.1", | |
| "keras_hub_version": "0.30.0.dev0", | |
| "parameter_count": 305607680, | |
| "date_saved": "2026-05-13@23:34:59", | |
| "tasks": [ | |
| "ImageClassifier" | |
| ] | |
| } |