README.md

---
license: apache-2.0
base_model:
- google/vit-base-patch16-224-in21k
pipeline_tag: image-classification
library_name: transformers
tags:
- Fire-Detection-engine
- Precision-98
- Classification
---
![ccccccccccccccccccccccccccc.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WP5cejlMirz3t6o2_YzVQ.png)

# **Fire-Detection-Engine**

The **Fire-Detection-Engine** is a state-of-the-art deep learning model designed to detect fire-related conditions in images. It leverages the **Vision Transformer (ViT)** architecture, specifically the `google/vit-base-patch16-224-in21k` model, fine-tuned on a dataset of fire and non-fire images. The model is trained to classify images into one of the following categories: "Fire Needed Action," "Normal Conditions," or "Smoky Environment," making it a powerful tool for detecting fire hazards.

```python
Classification report:

                    precision    recall  f1-score   support

Fire Needed Action     0.9708    0.9864    0.9785       808
 Normal Conditions     0.9872    0.9530    0.9698       808
 Smoky Environment     0.9818    1.0000    0.9908       808

          accuracy                         0.9798      2424
         macro avg     0.9799    0.9798    0.9797      2424
      weighted avg     0.9799    0.9798    0.9797      2424
```

![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/kDS5cVee2ZVOv92rY0lGw.png)


# **Mappers**

```python
Mapping of IDs to Labels: {0: 'Fire Needed Action', 1: 'Normal Conditions', 2: 'Smoky Environment'} 

Mapping of Labels to IDs: {'Fire Needed Action': 0, 'Normal Conditions': 1, 'Smoky Environment': 2}
```

# **Key Features**
- **Architecture**: Vision Transformer (ViT) - `google/vit-base-patch16-224-in21k`.
- **Input**: RGB images resized to 224x224 pixels.
- **Output**: Binary classification ("Fire Needed Action" or "Normal Conditions" or "Smoky Environment").
- **Training Dataset**: A curated dataset of fire place conditions.
- **Fine-Tuning**: The model is fine-tuned using Hugging Face's `Trainer` API with advanced data augmentation techniques.
- **Performance**: Achieves high accuracy and F1 score on validation and test datasets.