temi92 / wuunet_firesegm

wUUNet_fireSegm

The PyTorch and Keras implementations of wUUNet model designed for multiclass fire segmentation For more information read the paper

Installation

The model is tested on Ubuntu 18.04 workstation with NVidia RTX2070

You need to install cuda before installing python packages

sudo python3 ./setup.py install

We have collected a custom dataset of 6250 samples. You can extract it via:

python3 ./dataset.py

Using

In order to train the models run:

python3 ./train_wuunet.py

or

python3 ./train_unet.py

the parameters of training are hardcoded and you can change them directly in training scripts

In order to use *.ipynb evaluation notebooks run jupyter server, e.g.:

jupyter notebook

the source code is reusable for wide range of segmentation tasks as well as extendable by introducing new CNN models to solve the multiclass fire-segmentation task.

Optimization

Training and evaluation procedures are tightly coupled with storing to/getting data from Filesystem since those files are stored into

${project_root}/output

folder you can increase the perfomance of such tasks via linking the output directory manually to the appropriate dir located in SSD e.g.:

sudo ln -s /ssd/output ${project_root}/output

Results

Model	Binary Jaccard	Multiclass Jaccard
UNet FS 224 non-int	87.43 %	78.26 %
UNet FS 224 Gauss	87.96 %	79.15 %
UUNet FS 224 Gauss	89.92 %	79.91 %
wUUNet FS 224 Gauss	91.35 %	80.23 %