Instructions to use Chizzsam/shape-models-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Chizzsam/shape-models-checkpoints 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://Chizzsam/shape-models-checkpoints") - Notebooks
- Google Colab
- Kaggle
Download face/face_shape_model.tflite from Chizzsam/shape-models-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 8.89 MB
-
https://huggingface.co/Chizzsam/shape-models-checkpoints/resolve/main/face/face_shape_model.tflite
- Command line
-
hf download hf://Chizzsam/shape-models-checkpoints/face/face_shape_model.tflite
-
curl -L -o face_shape_model.tflite https://huggingface.co/Chizzsam/shape-models-checkpoints/resolve/main/face/face_shape_model.tflite
8.89 MB
- Xet hash:
- f7f46511e1792063d65af0872ef160780aa585b9ef89444687d1a58670b9ef82
- Size of remote file:
- 8.89 MB
- SHA256:
- 331c449932cfa8f06d8a62a14fcd3c44f8b556bd431766d152ad555282107abf
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