Feature Extraction
Keras
tensorflow
deep-learning
dimensionality-reduction
clustering
computer-vision
fire-detection
fire-severity
Instructions to use AbdullahImran/Fire-Feature-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use AbdullahImran/Fire-Feature-Analysis 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://AbdullahImran/Fire-Feature-Analysis") - Notebooks
- Google Colab
- Kaggle
Download som_tri_classification_clustering.npy from AbdullahImran/Fire-Feature-Analysis: direct link, hf CLI and curl.
- Browser
- Download file 80.1 kB
-
https://huggingface.co/AbdullahImran/Fire-Feature-Analysis/resolve/main/som_tri_classification_clustering.npy
- Command line
-
hf download hf://AbdullahImran/Fire-Feature-Analysis/som_tri_classification_clustering.npy
-
curl -L -o som_tri_classification_clustering.npy https://huggingface.co/AbdullahImran/Fire-Feature-Analysis/resolve/main/som_tri_classification_clustering.npy
80.1 kB
- Xet hash:
- 92c617e40cb0b6417d052d714dc60060e6fe34cc98fc38623b4cd7c3038c3baa
- Size of remote file:
- 80.1 kB
- SHA256:
- 502a645b7ffe18d286f64943e854534750876701eb73b6f94c93e7c57664125b
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