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_labels.json from Chizzsam/shape-models-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 487 Bytes
-
https://huggingface.co/Chizzsam/shape-models-checkpoints/resolve/main/face/face_shape_labels.json
- Command line
-
hf download hf://Chizzsam/shape-models-checkpoints/face/face_shape_labels.json
-
curl -L -o face_shape_labels.json https://huggingface.co/Chizzsam/shape-models-checkpoints/resolve/main/face/face_shape_labels.json
487 Bytes
| { | |
| "model_version": "3.1.0", | |
| "input": { | |
| "shape": [ | |
| 1, | |
| 224, | |
| 224, | |
| 3 | |
| ], | |
| "dtype": "float32", | |
| "range": [ | |
| 0.0, | |
| 255.0 | |
| ], | |
| "layout": "NHWC", | |
| "color_order": "RGB", | |
| "preprocessing": "none \u2014 normalization is baked into the model" | |
| }, | |
| "output": { | |
| "shape": [ | |
| 1, | |
| 5 | |
| ], | |
| "dtype": "float32", | |
| "type": "softmax" | |
| }, | |
| "labels": [ | |
| "Heart", | |
| "Oblong", | |
| "Oval", | |
| "Round", | |
| "Square" | |
| ] | |
| } |