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_head.ai from shkelqim2026/EfficientNet: direct link, hf CLI and curl.
- Browser
- Download file 178 kB
-
https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet_Architecture/efficientnet_backbone_head.ai
- Command line
-
hf download hf://shkelqim2026/EfficientNet/EfficientNet_Architecture/efficientnet_backbone_head.ai
-
curl -L -o efficientnet_backbone_head.ai https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet_Architecture/efficientnet_backbone_head.ai
178 kB
- Xet hash:
- ecbc00d3e814dcf916a96bf139abdf7039474ed708acebe0e8dd6bb4349bb442
- Size of remote file:
- 178 kB
- SHA256:
- e76f3f59f56291e1791e084fdae069ea22b7d4445c1a1c5cdae3d976d4ec57b8
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