Instructions to use sarasij907/emotion-detection-efficientnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use sarasij907/emotion-detection-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://sarasij907/emotion-detection-efficientnet") - Notebooks
- Google Colab
- Kaggle
Download kfold_results.csv from sarasij907/emotion-detection-efficientnet: direct link, hf CLI and curl.
- Browser
- Download file 225 Bytes
-
https://huggingface.co/sarasij907/emotion-detection-efficientnet/resolve/main/kfold_results.csv
- Command line
-
hf download hf://sarasij907/emotion-detection-efficientnet/kfold_results.csv
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curl -L -o kfold_results.csv https://huggingface.co/sarasij907/emotion-detection-efficientnet/resolve/main/kfold_results.csv
225 Bytes
| fold,val_loss,val_accuracy | |
| 1,1.1901252269744873,0.5581678748130798 | |
| 2,1.170552372932434,0.5696621537208557 | |
| 3,1.1538811922073364,0.5754092931747437 | |
| 4,1.2202069759368896,0.5442354679107666 | |
| 5,1.213152289390564,0.5506009459495544 | |