Classifying Images using Regional CNN

This program classifies images from the CIFAR-10 dataset using a Convolutional Neural Network (CNN). The images are preprocessed and normalized then used to train a CNN consisting of convolutional, max pooling, dropout, fully connected, and output layers.

In-depth analysis and examples can be found in dlnd_image_classification.ipynb.

Specifications

Dataset

The CIFAR-10 dataset consists of 5 batches, named data_batch_1, data_batch_2, etc.. Each batch contains the labels and images that are one of the following:

airplane
automobile
bird
cat
deer
dog
frog
horse
ship
truck

Parameters

image_recognition.py

You can view different example of the CIFAR-10 dataset by changing the values of the batch_id and the sample_id:

batch_id - id for a batch (1-5)
sample_id - id for a image and label pair in the batch

batch_id = 1
sample_id = 10
helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)

train_image_recognition.py

You can experiment with the CNN by altering:

depth - Alter the depths of the CNN layers using common memory sizes
- 64
- 128
- 256
- ...
epochs - number of training iterations
batch_size - the number of training examples utilized in one iteration. Make sure to change this according to your system's memory and GPU capacity
keep_probability - the probability of keeping node using dropout

Example Output

Command Line

python image_recognition.py

*You must press [enter] to continue after example image appears.

python train_image_recognition.py

License

The image_classification program is a public domain work, dedicated to using CC0 1.0.

nikjohn7/Image-classification-on-CIFAR-10-dataset-using-RCNN