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Abstract: Object detection is a widely studied area of computer vision that has seen significant improvements over the years. However, most of the existing methods rely on high-end GPUs or specialized hardware, making them impractical for real-time detection on mobile devices. In this paper, we present a novel approach to object detection using a mobile camera. Our method utilizes a light weight convolutional neural network architecture and employs transfer learning to achieve high accuracy while keeping computational complexity low. We evaluate our method on a dataset of common objects and achieve a mean average precision (mAP) of 0.85. We also demonstrate the practicality of our approach by implementing it on a mobile device and achieving real-time object detection.