Dog Breed Classification using Deep Learning

Task

In this project, we will be classifying the breed of the dog from the given photo of a dog as input.

Dataset- https://cainvas-static.s3.amazonaws.com/media/user_data/AmrutaKoshe/dog_photos.zip

The dataset consists of 5 selected different breeds of dogs. Each folder is named after a breed and contains around 120 images of that breed. Based on the given image, we need to classify the breed as one of the 5 breeds present.

The 5 different breeds are-

Preprocessing –

First, import all the required libraries –

Download the data and unzip it to access the images and labels from your notebook.

List all the folder names in your dataset and check the number of classifications to make (number of breeds present)

output:

[\'bulldog\', \'pug\', \'rottweiler\', \'german shepherds\', \'labrador\']
5

To understand our dataset better, display some images

display some images, to understand data set

Split the training dataset into train and validation set

Perform data augmentation by using ImageDataGenerator so that we can acquire more relevant data from the existing images by making minor alterations to the dataset.

Divide the training dataset into train set and validation set.

Output:

Found 459 images belonging to 5 classes.
Found 112 images belonging to 5 classes.

Training the model

compile and fit the model

Output:

Epoch 1/170
14/14 [==============================] - 2s 116ms/step - loss: 1.6112 - accuracy: 0.2277 - val_loss: 1.6012 - val_accuracy: 0.2411
Epoch 2/170
14/14 [==============================] - 1s 99ms/step - loss: 1.6091 - accuracy: 0.2482 - val_loss: 1.6041 - val_accuracy: 0.2411
Epoch 3/170
14/14 [==============================] - 1s 99ms/step - loss: 1.6014 - accuracy: 0.2365 - val_loss: 1.6013 - val_accuracy: 0.2411
...
...
...
Epoch 168/170
14/14 [==============================] - 1s 99ms/step - loss: 0.3052 - accuracy: 0.8899 - val_loss: 0.9355 - val_accuracy: 0.7054
Epoch 169/170
14/14 [==============================] - 1s 99ms/step - loss: 0.3965 - accuracy: 0.8454 - val_loss: 1.0105 - val_accuracy: 0.7054
Epoch 170/170
14/14 [==============================] - 1s 103ms/step - loss: 0.2919 - accuracy: 0.8839 - val_loss: 0.7971 - val_accuracy: 0.7768