Introduction to the COCO dataset

With applications such as object detection, segmentation, and captioning, the COCO dataset is widely understood by state-of-the-art neural networks. Its versatility and multi-purpose scene variation serve best to train a computer vision model and benchmark its performance.

In this post, we will dive deeper into COCO fundamentals, covering the following:

What is COCO?

The Common Object in Context (COCO) is one of the most popular large-scale labeled image datasets available for public use. It represents a handful of objects we encounter on a daily basis and contains image annotations in 80 categories, with over 1.5 million object instances. You can explore COCO dataset by visiting SuperAnnotate’s respective dataset section.

COCO dataset

Modern-day AI-driven solutions are still not capable of producing absolute accuracy in results, which comes down to the fact that the COCO dataset is a major benchmark for CV to train, test, polish, and refine models for faster scaling of the annotation pipeline.

On top of that, the COCO dataset is a supplement to transfer learning, where the data used for one model serves as a starting point for another.

COCO classes

COCO classes

What is it used for and what can you do with COCO?

The COCO dataset is used for multiple CV tasks:

COCO dataset
  • Object detection and instance segmentation: COCO’s bounding boxes and per-instance segmentation extend through 80 categories providing enough flexibility to play with scene variations and annotation types.
  • Image captioning: the dataset contains around a half-million captions that describe over 330,000 images.
  • Keypoints detection: COCO provides accessibility to over 200,000 images and 250,000 person instances labeled with keypoints.
  • Panoptic segmentation: COCO’s panoptic segmentation covers 91 stuff, and 80 thing classes to create coherent and complete scene segmentations that benefit the autonomous driving industry, augmented reality, and so on.
  • Dense pose: it offers more than 39,000 images and 56,000 person instances labeled with manually annotated correspondences.
  • Stuff image segmentation: per-pixel segmentation masks with 91 stuff categories are also provided by the dataset.

Dataset formats

COCO stores data in a JSON file formatted by info, licenses, categories, images, and annotations. You can create a separate JSON file for training, testing, and validation purposes.

Info: Provides a high-level description of the dataset.

“info”: {
    “year”: int,
    “version”: str,
    “description:” str,
    “contributor”: str,
    “url”: str,
    “date_created”: datetime 
}

“info”: {
    “year”: 2021,
    “version”: 1.2,
    “description:” “Pets dataset”,
    “contributor”: “Pets inc.”,
    “url”: “http://sampledomain.org”,
    “date_created”: “2021/07/19” 
}

Licenses: Provides a list of image licenses that apply to images in the dataset.

“licenses”: [{
    “id”: int,
    “name”: str,
    “url:” str
}]

“licenses”: [{
    “id”: 1,
    “name”: “Free license”,
    “url:” “http://sampledomain.org”
}]

Categories: Provides a list of categories and supercategories.

“categories”: [{
    “id”: int,
    “name”: str,
    “supercategory”: str,
    “isthing”: int,
    “color”: list
}]

“categories”: [
    {“id”: 1, 
     “name”: ”poodle”, 
     “supercategory”: “dog”, 
     “isthing”: 1, 
     “color”: [1,0,0]},
    {“id”: 2, 
     “name”: ”ragdoll”, 
     “supercategory”: “cat”, 
     “isthing”: 1, 
     “color”: [2,0,0]}
]

Images: Provides all the image information in the dataset without bounding box or segmentation information.

“image”: {
    “id”: int,
    “width”: int,
    “height”: int,
    “file_name: str,
    “license”: int,
    “flickr_url”: str,
    “coco_url”: str,
    “date_captured”: datetime
}

“image”: [{
    “id”: 122214,
    “width”: 640,
    “height”: 640,
    “file_name: “84.jpg”,
    “license”: 1,
    “date_captured”: “2021-07-19  17:49”
}]

Annotations: Provides a list of every individual object annotation from each image in the dataset.

“annotations”: {
    “id”: int,
    “image_id: int”,
    “category_id”: int
    “segmentation”: RLE or [polygon],
    “area”: float,
    “bbox”: [x,y,width,height],
    “iscrowd”: 0 or 1
}

“annotations”: [{
    ”segmentation”:
    {	
        “counts”: [34, 55, 10, 71]
        “size”: [240, 480]
    },
    “area”: 600.4,
    “iscrowd”: 1,
    “Image_id:” 122214,
    “bbox”: [473.05, 395.45, 38.65, 28.92],
    “category_id”: 15,
    “id”: 934
}]

“annotations”: [{
    ”segmentation”: [[34, 55, 10, 71, 76, 23, 98, 43, 11, 8]],
    “area”: 600.4,
    “iscrowd”: 1,
    “Image_id:” 122214,
    “bbox”: [473.05, 395.45, 38.65, 28.92],
    “category_id”: 15,
    “id”: 934
}]

Key points

Machines’ ability to stimulate the human eye is not as far-fetched as it used to be. In fact, the CV industry is expected to exceed $48.6 billion by 2022. The success of CV is credited to the training data that is fed to the model. The COCO dataset, in particular, holds a special place among AI accomplishments, which makes it worthy of exploring and potentially embedding into your model. We hope this article expands your understanding of COCO and fosters effective decision-making for your final model rollout. Don’t hesitate to reach out should you have more questions.

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