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_latent_representations_dimention_reduction.npy from AbdullahImran/Fire-Feature-Analysis: direct link, hf CLI and curl.
- Browser
- Download file 748 kB
-
https://huggingface.co/AbdullahImran/Fire-Feature-Analysis/resolve/main/autoencoder_latent_representations_dimention_reduction.npy
- Command line
-
hf download hf://AbdullahImran/Fire-Feature-Analysis/autoencoder_latent_representations_dimention_reduction.npy
-
curl -L -o autoencoder_latent_representations_dimention_reduction.npy https://huggingface.co/AbdullahImran/Fire-Feature-Analysis/resolve/main/autoencoder_latent_representations_dimention_reduction.npy
748 kB
- Xet hash:
- a29c7d7f53159572fe73acff71c72582785dcef41759db1db6c60a86546664e3
- Size of remote file:
- 748 kB
- SHA256:
- 3e3dcaf2933cbc6d9e7954a1d8f00eed4a218044b78f26815a86377ea7f2a203
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.