ANALYSIS OF DEEP LEARNING ALGORITHMS FOR IMAGE CLASSIFICATION

Image classification is one of the most significant applications of Deep Learning models.Deep Learning forms a subset of machine learning wherein the neural networks consist of more than three layers.There are various popular deep-learning approaches for image classification.In this paper, we have analyzed deep learning models and activation functions to compare their efficiency and accuracy.The dataset used consists of 15 classes of vegetables each having 1000 images for training, 200 images for validation, and 200 images for testing.Types of models analyzed here include Multi-layer Perceptron Model, CNN Model, and Pre-trained model.The different activation functions used are ReLU, Leaky ReLU, ELU, SELU, Sigmoid, and Tanh.The results of the empirical evaluation for image classification yield that a CNN model is better than a perceptron model as it achieved high accuracy and low loss in less iterations when compared with the perceptron model.Relu activation function yields maximum accuracy when used with CNN for image classification.