Instructions to use keras/swin_base_patch4_window7_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/swin_base_patch4_window7_224 with KerasHub:
import keras_hub import keras # Load ImageClassifier model image_classifier = keras_hub.models.ImageClassifier.from_preset( "hf://keras/swin_base_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_base_patch4_window7_224") - Keras
How to use keras/swin_base_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_base_patch4_window7_224") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42ab0545308827fc545e884371ad23dcbdf3e2dc5a48cc5642f99bfcbf96a3db
- Size of remote file:
- 352 MB
- SHA256:
- 891cc2c4231620008e6b8216d4e531ddc03987addd0fec9901a4b45cf9192fba
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