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 autoencoder_losses_tri_classification_dimention_reduction.npy from AbdullahImran/Fire-Feature-Analysis: direct link, hf CLI and curl.
- Browser
- Download file 168 Bytes
-
https://huggingface.co/AbdullahImran/Fire-Feature-Analysis/resolve/main/autoencoder_losses_tri_classification_dimention_reduction.npy
- Command line
-
hf download hf://AbdullahImran/Fire-Feature-Analysis/autoencoder_losses_tri_classification_dimention_reduction.npy
-
curl -L -o autoencoder_losses_tri_classification_dimention_reduction.npy https://huggingface.co/AbdullahImran/Fire-Feature-Analysis/resolve/main/autoencoder_losses_tri_classification_dimention_reduction.npy
168 Bytes
- Xet hash:
- 62d6416587fda432ff7fd5c089b1c412aedd66670a3a6176666405b608f8a0ea
- Size of remote file:
- 168 Bytes
- SHA256:
- 14bb228c7f23dc04bc45bd8b5a7b3cfc51ee4cb9be4258cf688a5aa65b6e2cc7
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