# Fire-Smoke-Dataset

**Repository Path**: bear4zcx/Fire-Smoke-Dataset

## Basic Information

- **Project Name**: Fire-Smoke-Dataset
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-21
- **Last Updated**: 2022-09-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# Fire-Flame-Dataset
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An image dataset for training fire and frame detection AI
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<strong> Fire-Flame-Dataset </strong> is a dataset collected in order to train machine learning model to recognize Fire, smoke, and neutral(images without fire or smoke).This a dataset containing about 3000 images and 3 classes which include:
* Fire 
* Smoke
* neutral 
<br>

There are 1000 images in each category and 900 for train and 100 for testing

### Download, Training and Prediction
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The Fire-Flame-Dataset is provided for download in the release section of this repository. You can download the dataset via this link [Fire-Flame-Dataset](https://github.com/DeepQuestAI/Fire-Smoke-Dataset/releases/download/v1/FIRE-SMOKE-DATASET.zip). 

The implementation code in which the model was train with has been provide in this repository. The model was trained with train with resnet50 and a accuracy of 85% on the test data was achieved. The python codebase is contained in fire_flame.ipynb. 

<strong>Some of the prediction results are shown below:</strong> <br>
![fire_1](./Assets/fire_1.jpg)
> ('Image of:', 'Class: Fire', 'Confidence score: 1.0') 

![fire_2](./Assets/fire_2.jpg)
> ('Image of:', 'Class: Fire', 'Confidence score: 0.990234375') 

![neutral_1](./Assets/neutral_1.jpg)
> ('Image of:', 'Class: Neutral', 'Confidence score: 0.99365234375') 

![neutral_2](./Assets/neutral_2.jpg)
> ('Image of:', 'Class: Neutral', 'Confidence score: 1.0') 

![smoke_1](./Assets/smoke_1.jpg)
> ('Image of:', 'Class: Smoke', 'Confidence score: 0.4462890625') 

![smoke_1](./Assets/smoke_2.jpg)
> ('Image of:', 'Class: Smoke', 'Confidence score: 0.9970703125') 

### Reqirements
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* Python 3
* Pytorch
* Numpy
* Matplotlib
* TorchFussion

### References
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* [Kaiming H. et al, Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385 )