Flower Classification using Deep Learning models

Deep learning techniques are used widespread for image recognition and classification problems. Gradually, deep learning architectures have modified to comprise more layers and become more robust model for classification problems. In this paper, the base VGG16 model is fine-tuned for the classification flowers into five categories, namely, Daisy, Dandelion, Sunflower, Rose and Tulip flowers. The fine-tuned VGG16 model is trained using 3520 flower images. The model is achieved a classification accuracy of 97.67% for validation set and 95.00% for testing dataset. The Kaggle dataset is used for training, validation and testing of the proposed fine-tuned VGG16 model. The goal of this work is to show that a proper modified VGG16 deep model, which is, pre-trained on ImageNet for image classification can be used for other image data set using very small dataset without over fitting. The VGG16 model uses mall size 3x3 filters.