---
library_name: keras
tags:
  - keras
  - tensorflow
  - deep-learning
  - feature-extraction
  - dimensionality-reduction
  - clustering
  - computer-vision
  - fire-detection
  - fire-severity
---

# Fire Feature Analysis

This repository contains trained models and generated artifacts from feature extraction, dimensionality reduction, representation learning, and clustering experiments performed as part of the fire detection and severity classification project.

## Contents

### Autoencoder
`autoencoder_model_dimention_reduction.keras` — a trained Keras autoencoder used in the dimensionality-reduction workflow.

### Autoencoder Latent Representations
`autoencoder_latent_representations_dimention_reduction.npy` — latent representations generated by the autoencoder.

### Autoencoder Losses
`autoencoder_losses_tri_classification_dimention_reduction.npy` — loss values generated during the autoencoder experiment.

### EfficientNet Feature Representations
`efficientnet_features_tri_classification_feature_extraction.npy` — feature representations extracted using EfficientNet.

### Fire Feature Representations
`fire_features_effnet_tri_classification_feature_extraction.npy` — fire-related feature representations generated during the feature-extraction workflow.

### SOM Clustering
`som_tri_classification_clustering.npy` — output of the Self-Organizing Map (SOM) clustering experiment.

## Loading the Autoencoder

```python
import tensorflow as tf

autoencoder = tf.keras.models.load_model(
    "autoencoder_model_dimention_reduction.keras"
)

autoencoder.summary()
```

## Loading the NumPy Artifacts

```python
import numpy as np

latent_representations = np.load(
    "autoencoder_latent_representations_dimention_reduction.npy"
)

losses = np.load(
    "autoencoder_losses_tri_classification_dimention_reduction.npy"
)

som_results = np.load(
    "som_tri_classification_clustering.npy"
)
```

## Analysis Workflow

The artifacts in this repository support experimentation involving:

1. Deep feature extraction
2. Representation learning
3. Dimensionality reduction
4. Latent-space analysis
5. Feature analysis
6. Clustering

## Project Context

This repository is part of a larger deep learning project containing:

- binary fire detection models
- fire severity classification models
- datasets
- notebooks
- feature extraction
- dimensionality reduction
- clustering
- recommendation generation

## Limitations

The generated NumPy artifacts are dependent on the original data preprocessing and model pipelines.

To correctly interpret these artifacts, the corresponding project code and preprocessing procedures should also be considered.

## License

No standardized open-source license has been specified for this repository.

Please refer to the original project and dataset terms before redistribution or commercial use.