Image Classification using Pre-Trained Convolutional Neural Network in COLAB

The object detection recognition is the heart today's research world. The main goal of this field is to train machines to understand the content of an image just like humans do. The various Machine learning models like Histogram of oriented Gradients, Support Vector Machine, Bag of features model, Viola-Jones algorithms were earlier used for object recognition. The system is trained to detect and identify new object in the field of object recognition. Using Deep learning technique, the system is trained enough so that it could detect and recognize when a new object is fed to the system. Using deep learning it is possible to train the system to classify all types of data. It could be text, images, video or sound. It is implemented using neural network architecture. In deep learning, network may consist of hundreds of layers for complex systems. In this work, system is trained with Alex-net a pre-trained neural network; classify the various input images such as animals and birds. Various types of animals and birds are used to train the system and finally a new object is given it can able to classify object as lion, bell pepper etc. The system is implemented in Google Colab a virtual VM, in which there is no need to install software's and very much useful for the research community provided by Google for the research community. The system is implemented using python code using Colab. GPU is a graphics processing unit (GPU) is a specialized system provided by Google for free to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. Citation: V. Neethidevan. "Image Classification using Pre-Trained Convolutional Neural Network in COLAB." Global Research and Development Journal For Engineering 5.7 (2020): 40 - 45.