The MS COCO dataset provides a wide range of realistic images, showing disorganized scenes with various backgrounds, overlapping objects, etc. Training computer vision models -it is not sufficient to train computer vision algorithms on professionally shot images, because they do not represent real world conditions.There are two primary use cases for the MS COCO dataset:

Using FiftyOne, an open source tool, to work with the dataset for computer vision projects.Using the COCO API, provided in Lua, MATLAB, and Python, to set up the dataset.Downloading the full dataset using the gsutil rsync utility, provided free by Google Cloud.You can download these datasets from the official project website. MS COCO is composed of several datasets created in different years-each one focuses on a different computer vision task, such as: Use it in any type of work, including commercial projects, crediting the original creators.Remix images with each other or external images.According to this license you are allowed to: It is provided under a Creative Commons Attribution 4.0 License. The MS COCO dataset is maintained by a team of contributors, and sponsored by Microsoft, Facebook, and other organizations. Dense pose -the dataset has more than 39,000 images containing over 56,000 humans, with every labeled person annotated with an instance id and a mapping between pixels representing that person’s body and a template 3D model.

Panoptic -full scene segmentation, indicating objects in the image according to 80 categories of “things” (cat, pen, fridge, etc.) and 91 “stuff” categories (road, sky, water, etc.).

“Stuff image” segmentation -pixel maps of 91 categories of “stuff”-amorphous background regions like walls, sky, or grass.

Keypoints- the dataset has more than 200,000 images containing over 250,000 humans, labeled with keypoints such as right eye, nose, left hip.

Captioning -natural language descriptions of each image.

Object detection -coordinates of bounding boxes and full segmentation masks for 80 categories of objects.

MC COCO provides the following types of annotations: The dataset contains annotations you can use to train machine learning models to recognize, label, and describe objects. MS COCO (Microsoft Common Objects in Context) is a large-scale image dataset containing 328,000 images of everyday objects and humans.

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