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 preprocessor.json from keras/vit_base_patch16_224_imagenet: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/keras/vit_base_patch16_224_imagenet/resolve/main/preprocessor.json
- Command line
-
hf download hf://keras/vit_base_patch16_224_imagenet/preprocessor.json
-
curl -L -o preprocessor.json https://huggingface.co/keras/vit_base_patch16_224_imagenet/resolve/main/preprocessor.json
1.78 kB
| { | |
| "module": "keras_hub.src.models.vit.vit_image_classifier_preprocessor", | |
| "class_name": "ViTImageClassifierPreprocessor", | |
| "config": { | |
| "name": "vi_t_image_classifier_preprocessor", | |
| "trainable": true, | |
| "dtype": { | |
| "module": "keras", | |
| "class_name": "DTypePolicy", | |
| "config": { | |
| "name": "float32" | |
| }, | |
| "registered_name": null | |
| }, | |
| "image_converter": { | |
| "module": "keras_hub.src.models.vit.vit_image_converter", | |
| "class_name": "ViTImageConverter", | |
| "config": { | |
| "name": "vi_t_image_converter", | |
| "trainable": true, | |
| "dtype": { | |
| "module": "keras", | |
| "class_name": "DTypePolicy", | |
| "config": { | |
| "name": "float32" | |
| }, | |
| "registered_name": null | |
| }, | |
| "image_size": [ | |
| 224, | |
| 224 | |
| ], | |
| "scale": [ | |
| 0.00784313725490196, | |
| 0.00784313725490196, | |
| 0.00784313725490196 | |
| ], | |
| "offset": [ | |
| -1.0, | |
| -1.0, | |
| -1.0 | |
| ], | |
| "interpolation": "bilinear", | |
| "antialias": false, | |
| "crop_to_aspect_ratio": true, | |
| "pad_to_aspect_ratio": false, | |
| "bounding_box_format": "yxyx" | |
| }, | |
| "registered_name": "keras_hub>ViTImageConverter" | |
| }, | |
| "config_file": "preprocessor.json" | |
| }, | |
| "registered_name": "keras_hub>ViTImageClassifierPreprocessor" | |
| } |