In this project, we will be classifying the breed of the dog from the given photo of a dog as input.
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-
- 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)
[\'bulldog\', \'pug\', \'rottweiler\', \'german shepherds\', \'labrador\'] 5
- To understand our dataset better, display some images
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.
Found 459 images belonging to 5 classes. Found 112 images belonging to 5 classes.
Training the model
- compile and fit the model
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
- Chose an image from the test set
Prediction is bulldog.
Hence, we have trained a sequential model to predict the breed of a dog with the image of the dog as the input.
Credit: Amruta Koshe
Also Read: Gemstone Classification using Deep Learning