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_classification_head.ai from shkelqim2026/EfficientNet: direct link, hf CLI and curl.
- Browser
- Download file 156 kB
-
https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet_Architecture/efficientnet_classification_head.ai
- Command line
-
hf download hf://shkelqim2026/EfficientNet/EfficientNet_Architecture/efficientnet_classification_head.ai
-
curl -L -o efficientnet_classification_head.ai https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet_Architecture/efficientnet_classification_head.ai
156 kB
- Xet hash:
- 053d8bce814dd358cb48a40be1ac358e34cd2d6c8b1f3ad55e960714b8c99bd5
- Size of remote file:
- 156 kB
- SHA256:
- 40b219c48501e5f6a8f912128c5ed15b1dd17b98a839c1027b744e1c7120ed75
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