Instructions to use sabarinath/Apollo-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use sabarinath/Apollo-V1 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://sabarinath/Apollo-V1") - Notebooks
- Google Colab
- Kaggle
A deep learning model for audio classification, designed to detect AI-generated music.
Model architecture : EfficientNetB0 (Transfer Learning)
Framework: TensorFlow / Keras
Task: Binary Classification (AI Music vs. Real Music)
Input: Mel Spectrograms (224x224) generated from audio files.
Training Data: Trained on a balanced dataset of thousands of modern songs from sources like the Free Music Archive, NCS, and the Suno AI dataset on Kaggle, all standardized to a consistent bitrate.
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