Discover the COCO dataset and why it is a valuable resource for your AI project!
COCO is a popular image dataset used to train AI. This article will cover what it is, why it is so popular and how you can start using it today!
Common Objects in Context, commonly known as COCO, is a large-scale dataset assembled for training computer vision AI models. It is a vast collection of images portraying people, animals, and commonplace objects in everyday settings. It was created by Microsoft in 2014 and now it has other organizations like Google and Facebook collaborating together.
COCO is a vast dataset that has lots of images. The collection includes:
There are also different types of labels in the collection. This allows the user to choose the correct annotation type for their project. The label types in the collection are:
Images shot in a controlled environment or professionally shot images are insufficient training material for computer vision models. This is because those images do not represent real-life conditions, which is what AI will most likely encounter. The real world is disorganized and dynamic and it can have overlapping objects of interest with a disorderly background. AI models need to be trained to manage chaotic environments if they are going to perceive the world they’ll be deployed to.
Expectation vs Reality
COCO offers us a unique dataset collection that is complete with images and labels that are set in everyday scenarios. The AI will be trained on images that are similar to the ones it will encounter in the real world and that is important. Its large size and variety of objects also make it an ideal dataset for training models. It is also possible to train an AI model on the COCO dataset at first and continue training it on a custom dataset. This way the model benefits from the best of both worlds!
If we want to compare AI algorithms, it is not as easy as deploying them on a sample testing dataset and comparing the results. It would be a bit like comparing apples and oranges. The algorithms have to be trained on the same training dataset and deployed on the same testing to get a fair comparison. This is where the COCO dataset comes in. COCO creates a level playing field for comparing AI models.
This was of great importance a few years ago as machine learning algorithms were the central area of improvement. However, improving the quality of datasets has become more critical now that AI algorithms have matured. Check out LabelFlow’s open image labeling platform that can boost your AI training today!
The dataset has been curated and labeled for everyone to use! Go to the download page on the COCO website and select the dataset you want for your project.
You can also use this python script to download the dataset directly to your preferred location. The COCO API is also available next to the download links so you can explore the COCO database you have downloaded. But you can also explore the dataset before you download it.
You can browse through some of the datasets using the COCO Explorer on the website. Select the categories you want to see and hit search. It will show you a few of the images from the dataset that match the criteria. Although, sometimes the results may not be what you expected.
Categories: Bird, Cake, and Table
The COCO dataset saves images in regular image formats, but the labels are saved in the COCO file format. The COCO file format is a specific JSON structure that describes the boundaries and attributes of labels and other metadata of the image. The COCO dataset is so popular that its file format is one of the most common object detection file types for training AI models.
LabelFlow integrates seamlessly into your existing AI training pipelines by supplying image annotations in the COCO format. We will add support for the YOLO soon.
Datasets with various object types set in real-world conditions are awesome. They empower every data scientist, every researcher, every AI startup to get started on their projects. COCO is one of the largest open datasets ever assembled that lets anyone train their AI model with ease.
When you want to take your AI to the next level, you need to train it with high-quality labeled images that have been curated to perfection. LabelFlow allows you to create your custom dataset so you can improve your AI model further!
Explore popular image annotation types and select the correct one for your AI project!