Instructions to use keras/vit_base_patch16_224_imagenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/vit_base_patch16_224_imagenet with KerasHub:
import keras_hub import keras # Load ImageClassifier model image_classifier = keras_hub.models.ImageClassifier.from_preset( "hf://keras/vit_base_patch16_224_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_base_patch16_224_imagenet") - Keras
How to use keras/vit_base_patch16_224_imagenet with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://keras/vit_base_patch16_224_imagenet") - Notebooks
- Google Colab
- Kaggle
Download task.weights.h5 from keras/vit_base_patch16_224_imagenet: direct link, hf CLI and curl.
- Browser
- Download file 347 MB
-
https://huggingface.co/keras/vit_base_patch16_224_imagenet/resolve/main/task.weights.h5
- Command line
-
hf download hf://keras/vit_base_patch16_224_imagenet/task.weights.h5
-
curl -L -o task.weights.h5 https://huggingface.co/keras/vit_base_patch16_224_imagenet/resolve/main/task.weights.h5
347 MB
- Xet hash:
- b4dedfa62ba27edc263f3d9d757a6c3ae5f450011e948aabe7865eff5bf459d8
- Size of remote file:
- 347 MB
- SHA256:
- 28b577bbd2aaabfb0d917ac1bfad83f93c34a4e63eccfd3bdad05afb7bea6f40
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