Instructions to use btehubsolutions/alertdrive-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use btehubsolutions/alertdrive-model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://btehubsolutions/alertdrive-model") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - image-classification | |
| - keras | |
| - tensorflow | |
| - alertdrive | |
| # AlertDrive AI Model | |
| Binary classification model for driver alert detection. | |
| ## Model Details | |
| - **Input:** 224x224x3 RGB images | |
| - **Output:** Binary classification (Alert/No Alert) | |
| - **Framework:** TensorFlow/Keras | |
| ## Usage | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| import tensorflow as tf | |
| # Download model | |
| model_path = hf_hub_download(repo_id="YOUR_USERNAME/alertdrive-model", filename="alertdrive_ai.keras") | |
| model = tf.keras.models.load_model(model_path) | |
| # Make prediction | |
| prediction = model.predict(image_array) | |
| ``` | |