Instructions to use MGeorgieff/ResNet151V2_GRADCAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MGeorgieff/ResNet151V2_GRADCAM with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://MGeorgieff/ResNet151V2_GRADCAM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c6f551047675df8e67d514e6729b83da6f8358baa29126798001ff0dbd844c8
- Size of remote file:
- 274 MB
- SHA256:
- adfa55b36e1f668b86f648d94ce26595616307aab3b617fb5b1589580111e984
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