Instructions to use keras/efficientnet_b2_ra_imagenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/efficientnet_b2_ra_imagenet with KerasHub:
import keras_hub import keras # Load ImageClassifier model image_classifier = keras_hub.models.ImageClassifier.from_preset( "hf://keras/efficientnet_b2_ra_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/efficientnet_b2_ra_imagenet") - Keras
How to use keras/efficientnet_b2_ra_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/efficientnet_b2_ra_imagenet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4ab1964613557829114de1be0014af0f19288b95272bd52c234ca20f7dd7a31
- Size of remote file:
- 37.5 MB
- SHA256:
- d052d51ac6a32460f7123ddc1dc6831dff036dc726f235e9318e5885e59f9d5f
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