Instructions to use DataScienceProject/Resnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DataScienceProject/Resnet 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://DataScienceProject/Resnet") - Notebooks
- Google Colab
- Kaggle
Download model_2.weights.h5 from DataScienceProject/Resnet: direct link, hf CLI and curl.
- Browser
- Download file 96 MB
-
https://huggingface.co/DataScienceProject/Resnet/resolve/main/model_2.weights.h5
- Command line
-
hf download hf://DataScienceProject/Resnet/model_2.weights.h5
-
curl -L -o model_2.weights.h5 https://huggingface.co/DataScienceProject/Resnet/resolve/main/model_2.weights.h5
96 MB
- Xet hash:
- 65d7ba1015106f7a9e4f186d7409a0885dffd3108005f7619a8a47c544cb4d91
- Size of remote file:
- 96 MB
- SHA256:
- 2bb30a1a6a3824eb47bc067d5c9b17bb8944c4efe2108a81973fb4aeee52d817
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