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 requirements.txt from DataScienceProject/Resnet: direct link, hf CLI and curl.
- Browser
- Download file 359 Bytes
-
https://huggingface.co/DataScienceProject/Resnet/resolve/main/requirements.txt
- Command line
-
hf download hf://DataScienceProject/Resnet/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/DataScienceProject/Resnet/resolve/main/requirements.txt
359 Bytes
| 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. |