Instructions to use keras/deit_tiny_distilled_patch16_224_imagenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/deit_tiny_distilled_patch16_224_imagenet with KerasHub:
import keras_hub import keras # Load ImageClassifier model image_classifier = keras_hub.models.ImageClassifier.from_preset( "hf://keras/deit_tiny_distilled_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/deit_tiny_distilled_patch16_224_imagenet") - Keras
How to use keras/deit_tiny_distilled_patch16_224_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/deit_tiny_distilled_patch16_224_imagenet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5141a5aa9573bbd88f9f73b893346fcb2b766cd44c77c4d1ed11d7f97383988d
- Size of remote file:
- 23.4 MB
- SHA256:
- 47dfb8c02bb7fc45e188ca1df64af236290115fb080ab3d9130a13fb2a9b6013
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.