Instructions to use keras/swin_tiny_patch4_window7_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/swin_tiny_patch4_window7_224 with KerasHub:
import keras_hub import keras # Load ImageClassifier model image_classifier = keras_hub.models.ImageClassifier.from_preset( "hf://keras/swin_tiny_patch4_window7_224", 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/swin_tiny_patch4_window7_224") - Keras
How to use keras/swin_tiny_patch4_window7_224 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/swin_tiny_patch4_window7_224") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3fd5785ded9ebc56ccdfcda6e24b0ce21c4e08b035d80905b42614b60e54edc
- Size of remote file:
- 110 MB
- SHA256:
- 56f9b3c3829cc4a24aafd90b46a5ab89ef132de1b89a8137b9ae5197f1ab9974
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