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:
- c09c47408f3474363e0099a3a4d84d1603e59048296d2762584a7fd51104a095
- Size of remote file:
- 15 MB
- SHA256:
- 38e7aa324463a508a047c7fed6380a25a0673a58d8096f69a8dc91dccd455c3e
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