Classification of Images using Transfer Learning

For better accuracy and faster convergence, image classification using Transfer Learning and deep feed-forward neural networks (DFNN) is important. We have used existing models in Transfer Learning that save training time, improve neural network performance, and do not require a lot of data. A number of image classification models have been developed so far that articulates the major issue concerned with recognition accuracy. Image recognition is the most critical problem encountered in the areas applying practical applications of Computer Vision. The attribute of Object Recognition governed by autonomous vehicles with robotic influences, obstacle or pedestrian detection system, etc are few of the practical examples dealing with recognition accuracy. Machine learning, especially neural networks like the (DFNN) that won image classification competitions, has received a lot of attention. Such a DFNN architecture model should be researched and investigated, according to this article. Using new image datasets to see if Transfer Learning will perform better in terms of precision and productivity. There are some similarities to state-of-the-art approaches. The DFNN can be tweaked by changing the total number of hidden layers, hidden neurons in each hidden layer, and the number of connections made in between layers.