Keras
English
audio
anomaly-detection
industrial
predictive-maintenance
acoustic-monitoring
dcase2020
arcface
transformer
tensorflow
Instructions to use Jvachier/dcase2020-task2-anomaly-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Jvachier/dcase2020-task2-anomaly-detection 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://Jvachier/dcase2020-task2-anomaly-detection") - Notebooks
- Google Colab
- Kaggle
Download version.json from Jvachier/dcase2020-task2-anomaly-detection: direct link, hf CLI and curl.
- Browser
- Download file 41 Bytes
-
https://huggingface.co/Jvachier/dcase2020-task2-anomaly-detection/resolve/main/version.json
- Command line
-
hf download hf://Jvachier/dcase2020-task2-anomaly-detection/version.json
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curl -L -o version.json https://huggingface.co/Jvachier/dcase2020-task2-anomaly-detection/resolve/main/version.json
41 Bytes
| {"emb_version": "v5", "run_tag": "v5.4"} | |