Short summary: This guide walks you from environment setup to a working PyTorch example that trains a Convolutional Neural Network (a pretrained ResNet) to recognize which object categories are present in a COCO image (multi-label image recognition). You’ll learn how to load COCO annotations, build a multi-label dataset, train with BCEWithLogitsLoss, evaluate average precision, and run inference. Run code snippet on a machine with Python + PyTorch.
Why COCO and What This Tutorial Does
The MS-COCO dataset is a large-scale dataset for object detection, segmentation and captioning; it contains hundreds of thousands of images and 80 common object categories (people, cars, cups, etc.). It’s a standard benchmark for object-level tasks, and we’ll reuse its annotations to turn detection-style labels into a multi-label image recognition task: for each image, predict which of the 80 categories appear. This is a practical way to use a CNN backbone (ResNet) and practice multi-label learning on a real dataset.