Approaches to build Object Detection Model

In object detection involves image processing in which we identify and locate objects within an image or video frame. Generally image processing in deep neural network is done with the help of convolutional neural networks (CNNs), using these we can either use a pre-built convolutional neural networks architecture model or we can create our custom convolutional neural networks architecture model to do object detection

• Creating & Training a Custom Object Detection Model:  To create and train a custom object detection model from scratch. We need to create a model architecture through which the model will learn to extract features like objects of interest to do so. We also required a large label data set in which we will be manually labeling the objects in images or a frame in a video, and we might also need to set up the layer weights in the CNN. This whole process will take a very long time and is hard for a beginner.
• Use a Pretrained Object Detection Model: Using pre-trained models will reduce the tedious work of creating model architectures and hyperparameter tuning. Leveraging these pre-trained models, we can tap into their expertise in object detection and fine-tune them for our specific application. This approach can lead to quicker results since these detectors have already been trained on thousands, if not millions, of images.

In November 2015, Google’s deep artificial intelligence research division introduced TensorFlow, a cutting-edge machine learning library initially designed for internal purposes. This open-source library revolutionized the field, which helped researchers and developers in building, training, and deploying machine learning models. With TensorFlow, the implementation of various machine learning algorithms and deep learning applications, including image recognition, voice search, and object detection, became seamlessly achievable. In this article, we will delve into the methodologies of object detection leveraging TensorFlow’s capabilities.