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COCO Dataset

Introduction to the COCO Dataset [2024 update]

Nov 8th, 2023

The computer vision research community employs standardized datasets to evaluate the performance of new models and improvements to existing ones. These datasets serve as benchmarks that can be applied universally across different models. This approach allows for the comparison of the effectiveness of various models, providing insights into which models outperform others.

 

In this article we delve into the The Common Objects in Context (COCO) dataset , a prime example of such a benchmarking dataset, extensively utilized within the computer vision research community. Specifically we will discuss :

 

  1. The COCO dataset
  2. Key characteristics of the COCO dataset
  3. Use-case of the
  4. COCO dataset
  5. COCO dataset class List
  6. Dataset formats
  7. Dataset explorer
The COCO dataset

1- The COCO dataset :

The MS COCO dataset, released by Microsoft in 2015 , is an extensive dataset designed for object detection, image segmentation, and captioning. Machine learning and computer vision experts widely adopt this dataset for a variety of computer vision endeavors. In the field of computer vision, a fundamental objective is to comprehend visual scenes, which encompasses tasks like identifying the objects present, pinpointing their positions in 2D and 3D space, determining object attributes, and elucidating the relationships between objects. Consequently, the dataset serves as a valuable resource for training algorithms related to object detection and classification.

COCO

2- Key Characteristics of the COCO Dataset:

In this section, we will showcase the pivotal attributes of the COCO dataset.

 

  • Abundant Object Instances: A dataset with a vast 1.5 million object instances.
  • Extensive Image Collection: Contains over 200,000 labeled images out of a total of 330,000.
  • Diverse Object Categories: Comprises 80 “COCO classes” encompassing easily labeled entities like people, cars, and chairs.
  • Human Pose Data: Features 250,000 individuals with 17 different keypoints, commonly used for pose estimation.
  • Multiple Captions: Each image is associated with five descriptive captions.

3- Use-case of the COCO dataset :

The COCO dataset serves as a versatile resource for various computer vision tasks. It is frequently harnessed for object detection, semantic segmentation, and keypoint detection. 

 

In this section we will delve into each of these problem types for a comprehensive understanding.

You want to know how to label your data ?

3.1- Object detection with COCO:

Each object within the dataset is annotated with a bounding box and a corresponding class label. This annotation proves invaluable for identifying the objects present within an image. As illustrated in the example below, different objects are being detected.

COCO 2020 Object Detection Task

3.2- Keypoint detection with COCO:

In keypoint detection, human subjects are annotated with key points of significance, including joints like the elbow and knee. These key points enable the tracking of specific movements, such as discerning whether a person is standing or sitting down. The COCO dataset includes annotations for over 250,000 individuals with their corresponding keypoints.

keypoints-splash

3.3- Semantic Segmentation with COCO:

Semantic segmentation involves the labeling of object boundaries with masks and the assignment of class labels to objects. This enables precise identification of the locations of various objects within a photo or video, offering a finer level of detail.

panoptic-splash

4- COCO Dataset Class List :

The COCO dataset covers 80 different class labels:

 

‘person’, ‘bicycle’, ‘car’, ‘motorcycle’, ‘airplane’, ‘bus’, ‘train’, ‘truck’, ‘boat’, ‘traffic light’, ‘fire hydrant’, ‘stop sign’, ‘parking meter’, ‘bench’, ‘bird’, ‘cat’, ‘dog’, ‘horse’, ‘sheep’, ‘cow’, ‘elephant’, ‘bear’, ‘zebra’, ‘giraffe’, ‘backpack’, ‘umbrella’, ‘handbag’, ‘tie’, ‘suitcase’, ‘frisbee’, ‘skis’,’snowboard’, ‘sports ball’, ‘kite’, ‘baseball bat’, ‘baseball glove’, ‘skateboard’, ‘surfboard’, ‘tennis racket’, ‘bottle’, ‘wine glass’, ‘cup’, ‘fork’, ‘knife’, ‘spoon’, ‘bowl’, ‘banana’, ‘apple’, ‘sandwich’, ‘orange’, ‘broccoli’, ‘carrot’, ‘hot dog’, ‘pizza’, ‘donut’, ‘cake’, ‘chair’, ‘couch’, ‘potted plant’, ‘bed’, ‘dining table’, ‘toilet’, ‘tv’, ‘laptop’, ‘mouse’, ‘remote’, ‘keyboard’, ‘cell phone’, ‘microwave’, ‘oven’, ‘toaster’, ‘sink’, ‘refrigerator’, ‘book’, ‘clock’, ‘vase’, ‘scissors’, ‘teddy bear’, ‘hair drier’, ‘toothbrush’

5- Dataset formats:

A COCO dataset encompasses five key sections, each contributing essential information for the dataset:

 

  • Info:  gives general information about the dataset.

“Info”:

 { “year”: int,

 “version”: str,

 “description:” str,

 “contributor”: str,

 “url”: str,

 “date_created”: datetime }

 

  • Licenses: Details about the licenses governing the images in the dataset.

“Licenses”:

 [{ “id”: int,

 “name”: str,

 “url:” str }]

 

  • Images : A comprehensive list of all the images contained in the dataset.

“image”: 

{ “id”: int,

 “width”: int,

 “height”: int,

 “file_name: str,

 “license”: int,

 “flickr_url”: str,

 “coco_url”: str,

 “date_captured”: datetime }

 

  • Annotations: A detailed list of annotations, which includes bounding boxes, encompassing all images 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 }

 

  • Categories: A comprehensive list of label categories utilized within the dataset.

“categories”: 

[{ “id”: int,

 “name”: str,

 “supercategory”: str,

 “isthing”: int,

 “color”: list }]

6- Dataset explorar :

To have a better understanding of the available data you can utilize the COCO dataset explorer. 

As it is shown in the next image, 420 pictures were found that have both a cat and a laptop. 

Dataset explorar

Conclusion:

In summary, the COCO dataset stands as a prominent and widely employed benchmark dataset within the field of computer vision. It presents a diverse array of images, meticulously annotated to facilitate tasks such as object detection, segmentation, and image captioning.