However, only 80 object categories of labeled and segmented images were released in the first publication in 2014. A machine learning practitioner can take advantage of the labeled and segmented images to create a better performing object detection model.Īs written in the original research paper, there are 91 object categories in COCO. COCO dataset provides the labeling and segmentation of the objects in the images. In everyday scene, multiple objects can be found in the same image and each should be labeled as a different object and segmented properly. However, it will be challenging to describe the environment where the photograph was taken without having other supplementary images that capture not only the person but also the studio or surrounding scene.ĬOCO was an initiative to collect natural images, the images that reflect everyday scene and provides contextual information. Looking at the photograph, we can only tell that it is an image of a person. A non-contextual, isolated image will be a close-up photograph of a person. Let’s say we want to detect a person object in an image. We can put an analogy to explain this further. As hinted by the name, images in COCO dataset are taken from everyday scenes thus attaching “context” to the objects captured in the scenes. Tl dr The COCO dataset labels from the original paper and the released versions in 20 can be viewed and downloaded from this repository.ĬOCO stands for Common Objects in Context. In this post, we will briefly discuss about COCO dataset, especially on its distinct feature and labeled objects. The names in the list include Pascal, ImageNet, SUN, and COCO. In the realm of object detection in images or motion pictures, there are some household names commonly used and referenced by researchers and practitioners. A good dataset will contribute to a model with good precision and recall. One important element of deep learning and machine learning at large is dataset.
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