1. Free or Not? Sort Machine Learning Datasets by Availability
  2. Datasets for Machine Learning Categorized by the Types of Data
    1. Computer Vision: Image and Video ML Datasets
    2. Natural Language Processing: Text and Audio Datasets
  3. Datasets for Machine Learning Based on the Types of ML and Annotation Tasks
  4. Large and Small Specific Types of Datasets for Machine Learning
  5. A Few More Words About Datasets for Machine Learning: Let’s Recap

In our recent article, we’ve talked about ML datasets. We offered their definition and info on what they were and why they were a crucial component for any AI project. We also covered the major features of great datasets and why both quality and quantity matter.

In a nutshell, machine learning datasets are required to properly train an ML algorithm for accurate prediction tasks of different difficulty levels. There are a variety of purposes for collecting (or finding) a good ML dataset. Yet it’s worth remembering that such datasets are usually quite expensive and hard to come by. For supervised and semi-supervised learning, a dataset should additionally be annotated. For unsupervised learning, while not requiring data labeling, creating high-quality datasets still may be costly and labor-intensive.

In this article, we’ll take a step further and give a short (yet hopefully exhaustive) summary of some popular and interesting datasets for machine learning complete with links and a feature overview for your convenience.

Free or Not? Sort Machine Learning Datasets by Availability

Open-source datasets

When talking about the lists of machine learning datasets, the first option that comes up is usually public datasets. These are open-source datasets for machine learning that are free to use by anyone for the purposes of ML research or training the algorithms for AI projects.

Despite the initial instinct, such sample datasets for machine learning are not rare. There are a variety of datasets available for the public, and there are resources that specialize in collecting such datasets and offering them for free download and use. It’s a simple and easy way to get the data you need to train and test your ML model, which is perfect for beginners.

However, it’s worth remembering that, while being free and requiring little to no effort to collect, these datasets are usually too basic or, on the contrary, quite unique. This means that they rarely can suit the purpose of a specific AI project. It’s worth remembering that such datasets were initially collected for different purposes. And while your project may benefit greatly from using a basic free dataset to practice machine learning, it might still need certain tweaks and changes.

Pro tip: the major concern here is to make sure that preparing a free dataset for machine learning will not take up more time and resources than collecting a fitting one from scratch.

However, looking through the databases of such sets is always a good place to start your search. Here are a few suggestions where you could find online ML datasets:

  • US 1, US 2, EU, and UK data projects are official sources of datasets that offer most various and official data from US, EU, and UK institutions. The fields include but are not limited to economics, education, health care, science, environment, etc. However, while the data is official, it might not be full and require further research.
  • For a wider range of general datasets, CMU offers quite a long list of datasets, big and small. On the other hand, if you need an interesting dataset for your machine learning project, try looking at Kaggle as it offers a wide range of niche datasets that will surprise you with their variety. But take care to check how clean your dataset of choice is: these datasets are commonly user-contributed.
  • For flexible, comprehensive, and convenient research, go to well-known resources like Google Dataset Search, AWS Registry of Open Data, Microsoft Azure Public Datasets, Reddit, or Github. These repositories offer great collections that arguable satisfy the most unique and wide range of machine learning purposes.

Now that we’ve covered a few of the benchmark dataset repositories, let’s dig a little deeper and find out how we can facilitate your search of a dataset depending on (1) the type of data that you have and (2) the type of ML or labeling task that you want the dataset to perform.

Datasets for Machine Learning Categorized by the Types of Data

Datasets for image, video, text, and audio data

There are different datasets that cater to your needs, depending on the type of data that you require (image, video, text, audio, or other). We’ll give but a few of the most well-known or those that we like best since there’s a lot of them and we cannot possibly cover all.

Pro tip: most of these datasets can also be used for deep learning tasks, which makes them invaluable for modern Ai projects.

Computer Vision: Image and Video ML Datasets

  • ImageNet is kind of a fall-back option for a machine learning dataset if you’re starting to work on a computer vision task.
  • Open Images from Google offers a generous choice of annotated images with over 6k labels. 
  • COIL100 is an interesting dataset of 100 objects captured from different angles, comprising together a 360-degree view of each object.
  • xView, a massive dataset for overhead images.
  • Color detection dataset for over 860 color names.
  • MNIST, a famous dataset of handwritten digits that can be separated into its own category as it boasts extensive popularity for the purposes of training and testing ML algorithms.
  • Kinetics-700, a video dataset of 700k YouTube videos, and YouTube8M that offers over 6 million.

Natural Language Processing: Text and Audio Datasets

You might also be looking for a specific format to download, like JSON datasets, which is also a great way to sort through the available and relevant datasets.

Datasets for Machine Learning Based on the Types of ML and Annotation Tasks

Datasets for different annotation tasks

Similar to the categorization of datasets based on the types of data, they are also often collected based on the types of tasks that need to be performed.

Image data serves as a great example to illustrate this fact as it is very popular, flexible, and widespread.

Large and Small Specific Types of Datasets for Machine Learning

There are also highly specialized datasets and samples that wouldn’t fit just any machine learning task. For a sweet finale, we’ll link you to a few of these specific datasets that just might suit your AI project’s purposes:

A Few More Words About Datasets for Machine Learning: Let’s Recap

Things to remember when looking for a suitable ML dataset

As you can see, there are a lot of machine learning datasets collected for a variety of purposes and based on very different categories, from the types of data to the types of annotation and machine learning tasks. So, if you are thinking about where to get the dataset for your AI project, it’d be wise first to take a look around: maybe the perfect dataset is already waiting for you.

A few things that you need to pay attention to are the following:

  1. Is it free or not? Open source datasets for machine learning are not rare. However, they may be too general or too specific to fit your project’s goal. Make sure you’re ready to make the necessary changes if you decide in favor of a ready-to-download dataset instead of collecting one yourself.
  2. What type of data do you need? Think about the purposes of your algorithm and what type of data you require. Then you can look through the available datasets to find the best fit.
  3. What type of machine learning and annotation tasks are you looking for? Facial recognition is very different from text classification or autonomous driving. The good news is that there are datasets for all of these, and many more, from general to specific tasks.
  4. Is your task very specific? Usually, the more specific the task, the harder it is to find the appropriate dataset that is ready to be used. Besides, designing an ML algorithm might also depend on the dataset to be used, so it’s best to work both on the algorithm and finding and processing the data simultaneously.

Also, consider if you need an annotated dataset (for supervised or semi-supervised machine learning tasks) or if you’ll need no labeling (unsupervised learning tasks). It may be so that you’ll find a suitable dataset that doesn’t have the necessary labels. In that case, you’ll need to spend time and resources on annotation.

It may be quite a fussy and labor-intensive task, however. If you wish to avoid the effort, don’t hesitate to contact us, and we’ll help you with both collecting and annotating the data that you need.

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