One of the most prominent large-scale tagged picture datasets accessible for public usage is the Common Object in Context (COCO). It comprises picture annotations in 80 categories, with approximately 1.5 million object instances, and represents a small number of items we see on a daily basis.

The COCO dataset is a major benchmark for CV to learn and train models for faster scaling of the pipeline. Modern Machine learning services are still not able to produce complete validity and reliability of data, which arises back to the fact that the COCO set of data is a major benchmark for CV to teach, check and modify frameworks for faster scalability of the annotation pipeline.

Furthermore, this dataset is a complement to learning, in which the data used for one model is utilized to start another.

The COCO dataset is utilized for a variety of CV tasks, including:

  • COCO gives access to approximately 200,000 photos and 250,000 human instances that have been tagged with key points.
  • COCO’s panoptic segmentation encompasses 91 things and 80 item classes, resulting in incoherent and full scene segmentations that aid the autonomous driving sector, augmented reality, and other applications.
  • Dense pose- it contains over 39,000 photos and 56,000 human instances that have been carefully annotated.
  • COCO’s bounding boxes with per segmentation extend over 80 categories, giving you plenty of room to experiment with different scene variants and annotation kinds.
  • Item picture segmentation: the collection also includes per-pixel segmentation masks for 91 different stuff types.
  • Image captioning: the collection includes over 500,000 descriptions that analyze over 330,000 pictures.

How to use the COCO dataset?

In just one command, the FiftyOne Dataset Zoo now enables partly obtaining and integrating COCO right into Python.

The following options are sent to the program to load COCO, letting you to choose the examples and tags you want:

  • label types- different types of labels that should be loaded. Values are things that are important to you. All labels are loaded by default, however not every sample will include every label type. If both max samples and label types are provided, each sample should include the label types supplied.
  • split and splits- a string or a list of strings specifying the divides to load. There are splits available: validation, test, and train.
  • max samples- is a limit on how many samples may be imported. All files are imported by default.
  • image ids- a list of individual image IDs to load, or the path to a file that contains that list.
  • classes- indicating which classes must be loaded. Only samples with a minimum of one instance of a certain class will be obtained.
  • max samples- is a limit on how many samples may be imported. All files are imported by default.
  • shuffle- boolean indicating if the samples should be imported in random order.

Most machine learning engineers have included rudimentary support for viewing small batches of photos into their workflows, such as using Tensorboard. However, such workflows do not enable you to dynamically search your dataset for specific cases of interest. This is a critical flaw to close since the easiest method to evaluate your model’s failure modes is to examine the performance of data and annotations by visualizing them and scrolling through several samples.

COCO vs Imagenet

By a country mile, this has been the most well-known picture dataset. The goal of the ImageNet project is to have 1000+ photos for every synset so that WordNet may have a visual hierarchy as well. ImageNet now includes approximately 14 million photos for nearly 22,000 sequences. Around the prominent item in over 1 million photos, hand-annotated bounding boxes have been added.

COCO, on the other hand, has about 200K photos, which isn’t a lot, but it stands out because of the problems that come with the extra characteristics it gives for each image, such as:

  • rather than merely bounding boxes of objects, object segmentation information
  • Per image, there are five written subtitles.

With that being said, you can execute both and pick the one that best suits your needs.


The COCO detection dataset API has become the de facto standard measure. Almost every object recognition research paper published in the last couple of years uses the COCO dataset semantic segmentation. While having a single statistic to show the feasibility at a top standard is really valuable, there is still more work to be done in practice.

You need to create trust in the model’s performance in a range of conditions while designing a simulation that will be used in a real-world scenario. Single samples and even specific forecasts to uncover success and failure situations will not offer you that information; the only way of understanding exactly where your model works effectively or where it works miserably is to focus on specific examples and even individual predictions.