Object detection with Faster R-CNN and PyTorch

Faster R-CNN is a technique to detect objects that utilizes regions proposal. In this course you will make use of Faster R-CNN that has been trained on COCO. COCO dataset. You will be taught how to recognize various objects by their names and make use of the probability of the predictions being correct.PyTorch is a well-known deep-learning framework open source that offers a flexible and effective platform for creating and developing neural networks. It provides a variety of functionalities and tools for different machine learning tasks, such as computer vision, neural language processing and much more. In this course you will learn about how to use the Faster R-CNN (Region Convolutional Neural Network) algorithm that is widely used to detect objects. Object detection is the process of the identification and location of objects in an image, as well as the class labels they correspond to. The faster R-CNN enhances earlier methods for detecting objects by introduction of the concept of a region proposal, which can help to determine the possible locations of objects within an image. The model that is used in this lab has been trained using COCO. COCO (Common objects in context) dataset. Training a model prior to training it using a vast dataset to discover general patterns and features that can be used to different tasks. Utilizing pre-trained models, you will benefit from the wisdom and knowledge gained from the training of large datasets.Using trained models that have been pre-trained Faster R-CNN algorithm, you'll be able to recognize objects based on their names. The model makes predictions about the location and presence of various objects in an image. In addition, you can explore the probability or certainty of the predictions for objects being accurate, which will assist in assessing the validity of the model's predictions.