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
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27505f6 | 1 2 3 4 5 6 | numpy: For numerical operations and handling arrays. tensorflow: For deep learning functionalities, including building and training models. pandas: For data manipulation and analysis. opencv-python: For image processing tasks. matplotlib: For plotting and visualizing data. scikit-learn: For machine learning utilities, including metrics and cross-validation. |