Instructions to use harsha163/CutMix_data_augmentation_for_image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use harsha163/CutMix_data_augmentation_for_image_classification with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://harsha163/CutMix_data_augmentation_for_image_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c6d61e84bc1bda11797f85e2a35d181f83c02e8916b8846c5ff5e8e8f7bd850
- Size of remote file:
- 19.4 kB
- SHA256:
- a69528aea21ea9b0cfb40d7066de8041def46ae28a425a818134fc1e7f5a16e5
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