Video Classification
Keras
deepfake-detection
explainability
grad-cam
efficientnet
faceforensics
tensorflow
Instructions to use mertkayacs/xdfdet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use mertkayacs/xdfdet 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://mertkayacs/xdfdet") - Notebooks
- Google Colab
- Kaggle
Download cutout-white.keras from mertkayacs/xdfdet: direct link, hf CLI and curl.
- Browser
- Download file 72.4 MB
-
https://huggingface.co/mertkayacs/xdfdet/resolve/main/cutout-white.keras
- Command line
-
hf download hf://mertkayacs/xdfdet/cutout-white.keras
-
curl -L -o cutout-white.keras https://huggingface.co/mertkayacs/xdfdet/resolve/main/cutout-white.keras
72.4 MB
- Xet hash:
- 947615fee8e921f481783a9ab20032d957fd857204317aab43bea627467c5848
- Size of remote file:
- 72.4 MB
- SHA256:
- f26b6d96d772b53e2bc8e1abdfb19c275eb93a592cf0c52bb43381424399ba1f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.