FireNet is an artificial intelligence project for real-time fire detection.

This is the first release of the FireNet. It contains an annotated dataset of 502 images splitted into 412 images for training and 90 images for validation.

>>> DOWNLOAD, TRAINING AND DETECTION:

The FireNet dataset is provided for download in the release section of this repository. You can download the dataset via the link below.

https://github.com/OlafenwaMoses/FireNET/releases/download/v1.0/fire-dataset.zip

We have also provided a ImageAI codebase to train a YOLOv3 detection model on the images and perform detection in mages and videos using a pre-trained model (also using YOLOv3) provided in the release section of this repository. The python codebase is contained in the fire_net.py file and the detection configuration JSON file for detection is also provided the detection_config.json. The pretrained YOLOv3 model is available for download via the link below.

https://github.com/OlafenwaMoses/FireNET/releases/download/v1.0/detection_model-ex-33--loss-4.97.h5


Running the experiment or detection requires that you have Tensorflow, and Keras, OpenCV and ImageAI installed. You can install this dependencies via the commands below.

- Tensorflow 1.4.0 (and later versions) Install or install via pip

 pip3 install --upgrade tensorflow 

- OpenCV Install or install via pip

 pip3 install opencv-python 

- Keras 2.x Install or install via pip

 pip3 install keras 

- ImageAI 2.0.3

pip3 install imageai --upgrade 

>>> Video & Prediction Results

Click below to watch the video demonstration of the trained model at work.

1. Joseph Redmon and Ali Farhadi, YOLOv3: An Incremental Improvement
   https://arxiv.org/abs/1804.02767