IMAGE RECOGNITION USING CONVOLITION NEURAL NETWORK

Abstract

The acknowledgment ofthe decent variety of materials that exist in the earth around us are a key visual ability that computer vision frameworks centre around. This project image recognition utilizes best in class Convolutional Neural Network (CNN) methods classifiers so as to perceive materials and examine the outcomes. Expanding on different broadly utilized material databases gathered, a choice of CNN structures is assessed to comprehend which is the best way to deal with recognition includes so as to accomplish remarkable results for the project. The outcomes consist of five material datasets with the accuracy of 82%, while applying another significant heading in computer vision. By restricting the measure of data extracted from the layer before the last fully connected layer, transfer learning goes for breaking down the commitment of shading data and reflectance to distinguish which fundamental feature choose the category the image has a place with. The accuracy of the project improves and with the comparison ofthe previous result it shows that performance of the project also improves particularly in the datasets which comprise of an extensive number ofimages