The purpose of the project is to study the previous methods of Object Detection using Deep Learning and propose a new method. The new model consists of three different techniques or processes: Regional Convolutional Neural Network (RCNN), Inception and ResNet. In object detection, the prime target of each method is to make sure maximum accuracy, high FPS, greater resolution and faster speed. But, due to the limitation of computational power it is not always possible to maintain a balance between these four. R-CNN in general is capable of handling high resolution images with a decent number of frames per second. In our method we introduced the concept of inception by dividing each large convolutional layer into smaller convolutional layers. And, by adding ResNet, we were able to get rid any extra layer that was not helping us in the gaining higher accuracy. Though we could achieve a low FPS, but the input image size was high resolution and the mean average precision was also high (almost close to SSD). We retrained the COCO and OpenImage datasets and results were decent enough. The model we build was based on the TensorFlow library.