PyTorch is a popular open-source deep learning framework that provides a flexible and efficient platform for building and training neural networks. It offers a wide range of tools and functionalities for various machine learning tasks, including computer vision, natural language processing, and more.

In this lab, you will explore the Faster R-CNN (Region Convolutional Neural Network) algorithm, which is a widely used method for object detection. Object detection involves identifying and localizing objects within an image, along with their corresponding class labels. Faster R-CNN improves upon previous object detection methods by introducing the concept of region proposal, which helps to identify potential object locations in an image.

The Faster R-CNN model used in this lab is pre-trained on the COCO (Common Objects in Context) dataset. Pre-training involves training a model on a large dataset to learn general features and patterns that can be applied to various tasks. By utilizing pre-trained models, you can benefit from the knowledge and insights gained from training on extensive datasets.

Using the pre-trained Faster R-CNN model, you will learn how to detect objects by their names. The model provides predictions for the presence and location of different objects within an image. Additionally, you will explore the likelihood or confidence of the object predictions being correct, which can help in understanding the reliability of the model's detections.

By working with pre-trained models and understanding their predictions, you will gain insights into the capabilities of object detection algorithms and their applications in computer vision tasks.

Overall, this lab will provide you with hands-on experience in using PyTorch, pre-trained models, and the Faster R-CNN algorithm to perform object detection and analyze the likelihood of object predictions.

After finishing this project you will be able to: