#Intel Landscape Image Classification | A Network Architecture comparison

##Introduction:

The intel landscape image classification dataset is quite similar to the previously explored ICFAR10 dataset. It consists of roughly 25k 150x150px images of 6 different categories.

The dataset was initially published (https://datahack.analyticsvidhya.com/) by Intel to host a Image classification Challenge.

The one used here can be found on kaggle to offer easy integration into colab:

https://www.kaggle.com/puneet6060/intel-image-classification

###Goals:

The main goal of this project was to first implement different architectures learnt throughout the course and beyond, to get a better feel for the different limitations of the architectures:

1. Logistic Regression
2. Feed Forward NN
3. Simple Convolutional Neural Network
4. Residual Neural Network (Resnet 9)
5. Residual Neural Network Variation (Resnet18)

After benchmarking the different architectures the best one is to be picked and augmented with different technics to achive a accuracy as close as possible to the one achieved in the competition (96%)