Skip to content

NotHotDog using Convolutional Neural Network.It achieved about 79% accuracy with only 800 images.

Notifications You must be signed in to change notification settings

ajayrao80/NotHotDogCNN

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 

Repository files navigation

NotHotDogCNN

NotHotDog using Convolutional Neural Network

Previously, I tried to get a Neural Network classify betweeen a hot dog and everything else. It was a simple neural network with just 1 hidden layer.

In this project, I tried the same with a Convolutional Neural Network. It contains 2 convolutional layers trained against a set of 800 examples.

It achieved about 79% accuracy which is pretty impressive considering it had only 800 images to train on. (On a simple neural network it achieved about 55% accuracy.) So this explains why Convolutional Neural Networks are used in image recognition.

P.S. image_preprocessing.py file is the same as the previous one excpet this time it takes RGB color of every pixels to build the training data.

About

NotHotDog using Convolutional Neural Network.It achieved about 79% accuracy with only 800 images.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages