Multiple Object Detection using Deep Learning

In the cutting edge and quick world, finding a precise and proficient object recognition for headway in Computer vision frameworks has been integral part. With the emerge offaster (due to faster and better hardware to) and increasingly accurate deep learning techniques the prediction for finding a good and accurate model has been justified. In this undertaking, we are actually aiming to incorporate best possible technique to tackle object detection with goal of achieving higher accuracy in our Multiple Object Detection project. The major motivation in our task was to making tremendous contribution to increase the accuracy of the object detection. In this venture, we utilize four models to tackle the problem, mainly Mask- RCNN (Mask Region Convolution Neural Network), Faster-RCNN (Faster Region Convolution Neural Network), SSD (Single Shot MultiBox Detection) and YOLO (You look only once) trained on the most publicly and freely accessible dataset (MS-COCO).