betoavila / doggonet

CNN for image clasification featuring my 4 dogs (Katy, Luna, Jack and Volt) with 1000 pictures each hope you can see who's who

DoggoNet

This is a CNN for image clasification of 4 dogs using a set of 4000 pictures

This code is divided in 4 stages:

1. Image preprocessing
2. Creation of numpy datasets
3. Model definition, testing and fitting using Keras
4. GUI creation to implement model using tkinter

1. Image processing

This part rotates and resizes the images to a suitable image size to be computed swiftly. 300x225 px is the size selected and all images are rotated to be in a horizontal view. This part only uses PIL and os modules.

2. Creation of numpy datasets

This section creates train and test sets in .npz format files to store the pictures and to prevent recalculation of datasets.

3. Model definition, testing and fitting using Keras

I created the following architecture:

Layer	(type)	Output Shape	Param
conv2d	(Conv2D)	(None, 215, 290, 64)	23296
max_pooling2d	(MaxPooling2D)	(None, 53, 72, 64)	0
conv2d_1	(Conv2D)	(None, 47, 66, 48)	150576
max_pooling2d_1	(MaxPooling2	(None, 15, 22, 48)	0
conv2d_2	(Conv2D)	(None, 13, 20, 32)	13856
max_pooling2d_2	MaxPooling2	(None, 6, 10, 32)	0
flatten	(Flatten)	(None, 1920)	0
dropout	(Dropout)	(None, 1920)	0
dense	(Dense)	(None, 64)	122944
dense_1	(Dense)	(None, 64)	4160
dense_2	(Dense)	(None, 64)	4160
dense_3	(Dense)	(None, 4)	260

Total params: 319,252

Trainable params: 319,252

Non-trainable params: 0

I also used Adam optimizer with the following parameters (which could be tuned and improved): opt = keras.optimizers.Adam(learning_rate=0.0013, beta_1=0.87, beta_2=0.9, epsilon=1.9e-07)

It achieved from 97% to 99% accuracy. Other architectures (more sophisticated) were not tested due to hardware limitations.

4. GUI creation to implement model using tkinter

To show the results I created a simple GUI using tkinter