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
File size: 1,781 Bytes
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"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"
} |