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_Architecture/efficientnet_backbone.png from shkelqim2026/EfficientNet: direct link, hf CLI and curl.
- Browser
- Download file 220 kB
-
https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet_Architecture/efficientnet_backbone.png
- Command line
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hf download hf://shkelqim2026/EfficientNet/EfficientNet_Architecture/efficientnet_backbone.png
-
curl -L -o efficientnet_backbone.png https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet_Architecture/efficientnet_backbone.png
220 kB

- Xet hash:
- a43e7f06fb59e95737e42571a510cb20e28a3bbcad2bce52b74f68623f31af0d
- Size of remote file:
- 220 kB
- SHA256:
- 4c3026d28375fb3fa5baa6ce6ba208de2f93184126a16b89797b5bb7176c5c27
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