Application of Convolutional Neural Network for Image Classification on Pascal VOC Challenge 2012 dataset
This work addresses image classification for computer vision researchers, but it is incremental as it applies existing methods to a standard dataset.
The researchers tackled image classification on the Pascal VOC Challenge 2012 dataset using convolutional neural networks, achieving a validation accuracy of 85.6% and a testing accuracy of 85.24%.
In this project we work on creating a model to classify images for the Pascal VOC Challenge 2012. We use convolutional neural networks trained on a single GPU instance provided by Amazon via their cloud service Amazon Web Services (AWS) to classify images in the Pascal VOC 2012 data set. We train multiple convolutional neural network models and finally settle on the best model which produced a validation accuracy of 85.6% and a testing accuracy of 85.24%.