/Improved_VGG16

Primary LanguagePython

Improved VGG-16

Introduction

In this project, I have tried to increase accuracy rate by gathering DAG-CNN architecture and Atrous convolution pyramid. After the models are trained, there are two different ensemble methods I have applied. First one is common one that gets the most common prediction among all the models and the second one is Logistic Regression that trains a model to predict correctly.

References

  1. On the use of DAG-CNN architecture for age estimation with multi-stage features fusion
  2. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
  3. AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images

NOTE: Not much time was spent on hypertuning.