cifar10-vgg16

Description

CNN to classify the cifar-10 database by using a vgg16 trained on Imagenet as base. The approach is to transfer learn using the first three blocks (top layers) of vgg16 network and adding FC layers on top of them and train it on CIFAR-10.

Training

Trained using two approaches for 50 epochs:

Keeping the base model's layer fixed, and
By training end-to-end

First approach reached a validation accuracy of 95.06%. Second approach reached a validation accuracy of 97.41%.

Files

Source Files:

vgg_transfer.py - The main file with training
vgg.py - Modified version of Keras VGG implementation to change the minimum input shape limit for cifar-10 (32x32x3)

Outputs:

outputs/output_1.txt
outputs/output_2.txt

Trained Models:

Approach 1
Approach 2

Tensorboard graphs (Appoach 2): Validation Accuracy:

Training Accuracy: