Instructions to use nhradek/FLUX-Detection-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use nhradek/FLUX-Detection-Classifier with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://nhradek/FLUX-Detection-Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cbf857f7ffd36d7cb12f22e37aa314acf1f8c565d7a919c45b743d5ea2cf0ee8
- Size of remote file:
- 632 MB
- SHA256:
- ac8a9cf4437bb7eee0e4047748c754da445096afd833ba09be897407068677f1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.