GRD Journals- Global Research and Development Journal for Engineering | Volume 5 | Issue 7 | June 2020 ISSN- 2455-5703
Image Classification using Pre-Trained Convolutional Neural Network in COLAB V. Neethidevan Assistant Professor Department of MCA Mepco Schlenk Engineering College, Sivkaasi
Abstract 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 pretrained 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. Keywords- Machine Learning, Colab, Deep Learning, Object Detection, Image Classification
I. INTRODUCTION A. Convolutional Neural Network Artificial Neural Networks (ANNs) are computational processing systems of which are heavily inspired by way biological nervous systems (such as the human brain) operate. ANNs are mainly comprised of a high number of interconnected computational nodes (referred to as neurons), of which work interweave in a distributed fashion to collectively learn from the input in order to optimize its final output. The basic structure of an ANN can be modelled as shown in Figure 1.
Fig. 1: Basic structure of an ANN
The input could be loaded, usually in the form of a multidimensional vector to the input layer of which will distribute it to the hidden layers. The hidden layers will then make decisions from the previous layer and weigh up how a stochastic change within itself detriments or improves the final output, and this is referred to as the process of learning. B. Convolutional Neural Networks (CNNs) Convolutional Neural Networks (CNNs) are analogous to traditional ANNs in that they are comprised of neurons that selfoptimize through learning. Each neuron will still receive an input and perform an operation (such as a scalar product followed by a non-linear function) - the basis of countless ANNs. From the input raw image vectors to the final output of the class score, the entire network will still express a single perceptive score function (the weight). The last layer will contain loss functions associated with the classes, and all of the regular tips and tricks developed for traditional ANNs still apply. The only notable
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