Instructions to use shkelqim2026/EfficientNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use shkelqim2026/EfficientNet with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://shkelqim2026/EfficientNet") - Notebooks
- Google Colab
- Kaggle
Download efficientnet_b2_final.keras from shkelqim2026/EfficientNet: direct link, hf CLI and curl.
- Browser
- Download file 100 MB
-
https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/efficientnet_b2_final.keras
- Command line
-
hf download hf://shkelqim2026/EfficientNet/efficientnet_b2_final.keras
-
curl -L -o efficientnet_b2_final.keras https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/efficientnet_b2_final.keras
100 MB
- Xet hash:
- 66944565374eba529348510c127cbdfc7b1e6c7183cd46c047e4de9e36f7bb4e
- Size of remote file:
- 100 MB
- SHA256:
- dd7065efd35c336d59e0978a468a8dfc34be462e2fbeab8f290a6ccb782ab729
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