Pretrained CNN Models on Smoke Dataset

Requirements:

Python 2.7+
Keras
Tensoflow
H5py

Training parameters:

Batch size = 10
100 epochs
RMSProp optimizer (learning rate = 0.001)
Training and evaluation sets: http://staff.ustc.edu.cn/~yfn/vsd.html (Sets 1 to 4)

Paper:

@INPROCEEDINGS{8004998, 
author={A. Filonenko and L. Kurnianggoro and K. H. Jo}, 
booktitle={2017 10th International Conference on Human System Interactions (HSI)}, 
title={Comparative study of modern convolutional neural networks for smoke detection on image data}, 
year={2017}, 
volume={}, 
number={}, 
pages={64-68}, 
keywords={neural nets;object detection;AlexNet;CNN;ImageNet dataset;Inception-V3;Inception-V4;ResNet;VGG;Xception;image data;inception-based networks;modern convolutional neural networks;smoke detection task;Biological neural networks;Convolution;Graphics processing units;Hardware;Neurons;Training;Videos}, 
doi={10.1109/HSI.2017.8004998}, 
ISSN={}, 
month={July},}

Preprocessed Yuan's smoke dataset (5GB)

AlexNet:

Inception-V3:

Inception-V4:

ResNet-50:

VGG-16:

VGG-19:

Xception:

License

GPL 3