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:
GPL 3