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:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://AbdullahImran/Fire-Feature-Analysis") - Notebooks
- Google Colab
- Kaggle
Download efficientnet_features_tri_classification_feature_extraction.npy from AbdullahImran/Fire-Feature-Analysis: direct link, hf CLI and curl.
- Browser
- Download file 120 MB
-
https://huggingface.co/AbdullahImran/Fire-Feature-Analysis/resolve/main/efficientnet_features_tri_classification_feature_extraction.npy
- Command line
-
hf download hf://AbdullahImran/Fire-Feature-Analysis/efficientnet_features_tri_classification_feature_extraction.npy
-
curl -L -o efficientnet_features_tri_classification_feature_extraction.npy https://huggingface.co/AbdullahImran/Fire-Feature-Analysis/resolve/main/efficientnet_features_tri_classification_feature_extraction.npy
120 MB
- Xet hash:
- c25a89414175e3ac298834008021f299fde48eb25336d4cf107eb3429882ef9e
- Size of remote file:
- 120 MB
- SHA256:
- 865f47858c79599b5cba38b92cfb76719d6e679264a43b18f4f74b2f431ed46a
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