A dog breed classification model created using CNN with InceptionV3 pretrained weights .
A dog breed classification model created using CNN with InceptionV3 pretrained weights .
This model is completely built in Python3
Libraries used :
Numpy
Pandas
Matplotlib
OpenCV
Tensorflow
Keras
The dataset can be downloaded from following links and should be put inside “input” folder in the main directory.
https://www.kaggle.com/keras/inceptionv3
https://www.kaggle.com/c/dog-breed-identification/data
Inceptionv3 is a convolutional neural network for assisting in image analysis and object detection, and got its start as a module for Googlenet. It is the third edition of Google’s Inception Convolutional Neural Network, originally introduced during the ImageNet Recognition Challenge. Just as ImageNet can be thought of as a database of classified visual objects, Inception helps classification of objects in the world of computer vision. One such use is in life sciences, where it aids in the research of Leukemia.
The original name (Inception) was codenamed this way after a popular “we need to go deeper” internet meme went viral, quoting a phrase from Inception film of Christopher Nolan.
Inception Layer is a combination of all those layers (namely, 1×1 Convolutional layer, 3×3 Convolutional layer, 5×5 Convolutional layer) with their output filter banks concatenated into a single output vector forming the input of the next stage.
The output looks like 
