Instructions to use courte/Car_Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use courte/Car_Vision with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://courte/Car_Vision") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "EfficientNetB0" | |
| ], | |
| "task": "feature-extraction", | |
| "model_type": "efficientnet", | |
| "image_size": 224, | |
| "num_channels": 3, | |
| "num_labels": 1000, | |
| "pooling": "avg", | |
| "classifier_activation": "softmax" | |
| } |