The COCO dataset contains over 200K images with more than 80 object categories. It provides annotations for object detection (bounding boxes), instance segmentation, and keypoint detection. The dataset is organized into three main parts: training, validation, and test sets.

The full object detection labeled dataset is made available here, ensuring researchers have access to the most comprehensive data for their experiments. With that said, COCO has not released their test set annotations, meaning the test data doesn't come with labels. Thus, this data is not included in the dataset.

Because the model was pretrained on COCO, it will recognize common objects in your images right away. This simple Python script demonstrates the core idea: load a model, run it on an image, and draw the results. From here, you can build more advanced apps (real-time video detection, object tracking, etc.) or fine-tune on your own dataset.

The Microsoft COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset, making it a popular choice for training and evaluating image detection models. PyTorch, a powerful deep learning framework, provides convenient tools and pre - trained models for working with the COCO dataset.