Multi-label Image Classification using Automated Approach.
The complete description of dataset is given on http://lamda.nju.edu.cn/data_MIMLimage.ashx. The dataset contains 2000 natural scenes images.
Resized all images to 100 by 100 pixels and created two sets i.e train set and test set. Train set contains 1600 images and test set contains 200 images.
As this is multi label image classification, the loss function was binary crossentropy and activation function used was sigmoid at the output layer. Keras doesn't have provision to provide multi label output so after training there is one probabilistic threshold method which find out the best threshold value for each label seperately, the performance of threshold values are evaluated using Matthews Correlation Coefficient and then uses this thresholds to convert those probabilites into one's and zero's.
Command: python visualization.py link: localhost:5000
Github: https://github.com/keras-team/keras
This code is provided under the MIT License.
Copyright 2019 Yasrub Bashir Malik
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