Keras
Portuguese
English
wifi-csi
fall-detection
human-activity-recognition
tensorflow
cnn-lstm
esp32
edge-ai
tensorflowjs
Instructions to use ElementalDLC37/flashview-csi-fall-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ElementalDLC37/flashview-csi-fall-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://ElementalDLC37/flashview-csi-fall-detection") - Notebooks
- Google Colab
- Kaggle
Download model.keras from ElementalDLC37/flashview-csi-fall-detection: direct link, hf CLI and curl.
- Browser
- Download file 278 kB
-
https://huggingface.co/ElementalDLC37/flashview-csi-fall-detection/resolve/main/model.keras
- Command line
-
hf download hf://ElementalDLC37/flashview-csi-fall-detection/model.keras
-
curl -L -o model.keras https://huggingface.co/ElementalDLC37/flashview-csi-fall-detection/resolve/main/model.keras
278 kB
- Xet hash:
- 5353d8b12409e190b82d55f9c866cde9b1dc8462fc2f841f73d6160e3287a652
- Size of remote file:
- 278 kB
- SHA256:
- 69660e503dbec3d21eb4e7b99ee8b8bb9eeaeac3f7b2cce3d07d05c2bcc3e503
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.