65+ Best Free Datasets for Machine Learning

Open Dataset Aggregators

“Where can I get free datasets for machine learning?” you might ask yourself. 

Look no further. 

Here’s the list of the best open dataset finders that you can use to browse through a wide variety of niche-specific datasets for your data science projects.

Let’s jump right into it.

Kaggle

A data science community with tools and resources which include externally contributed machine learning datasets of all kinds. From health, through sports, food, travel, education, and more, Kaggle is one of the best places to look for quality training data.

Google Dataset Search

A search engine from Google that helps researchers locate freely available online data. It works similarly to Google Scholar, and it contains over 25 million datasets. You can find here economic and financial data, as well as datasets uploaded by organizations like WHO, Statista, or Harvard.

UCI Machine Learning Repository

One of the oldest dataset aggregators on the web. All datasets are user-contributed, and you can download them from the UCI Machine Learning Repository website without registration. They are categorized by task, attribute, data type, and area of expertise.  

OpenML

OpenML is an online machine learning platform for sharing and organizing data with more than 21.000 datasets. It’s regularly updated and it automatically versions and analyses each dataset and annotates it with rich meta-data to streamline analysis. 

DataHub

A collection of thousands of machine learning datasets from financial market data, macroeconomic data, and population growth to cryptocurrency prices. You can access DataHub without any registration.

Papers with Code

A community project with free and open resources, currently including 3937 datasets for data science and machine learning, including natural language processing tasks. You can easily filter them by modality, task, or language.

VisualData

VisualData is search engine for computer vision datasets. You can easily filter them by category, date, popularity or use a search box to find a theme-specific dataset. A great source of datasets for image classification, image processing, and image segmentation projects.

You can start annotating your image and video data with V7 for free.

Public Government Datasets for Machine Learning

Leveraging demographic data can help governments to improve the well-being of citizens and the economy at scale. Using public government data to train machine learning models can help discover patterns, identify trends, and detect anomalies. 

Those predictive models can, in turn, help prevent some of the social and cultural issues like population decline or migration. 

Here’s the list of chosen public datasets that you can use for your machine learning projects.

Data.gov

The US government’s open data site. You can filter it by various industries like healthcare, climate, education, etc. Be aware that much of the open-source data on data.gov might require additional research.

Data.europa.eu

The point of access to public data published by the EU institutions, agencies, and other entities. Data.europa.eu contains data related to economics, agriculture, education, employment, climate, finance, science, etc.

World Bank 

The open data from the World Bank that you can access without registration. It contains data concerning population demographics, macroeconomic data, and key indicators for development. A great source of data to perform data analysis at a large scale.

US Healthcare Data

Statistics and datasets for health care and public health. In US Healthcare Data you can find data about population health, diseases, drugs, and health plans collected from the FDA and USDA Food composition databases.

The US National Center for Education Statistics 

Nces.ed.gov is a website with data on educational institutions and education demographics in the U.S. and internationally.

The UK Data Service

www.ukdataservice.ac.uk is a platform that provides access to over 7,000 digital data collections for research and teaching purposes. You can find here economic and social data from the Economic and Social Data Service (ESDS), Census Programme, and others, including some international data sets. 

Data USA

A free platform with the most comprehensive visualization of U.S. public data.  

Machine Learning Datasets for Finance and Economics

Open financial and economic datasets are a great source of information for your machine learning projects related to the financial sector. 

Thanks to the vast quantities of financial records collected over decades, you can train your models using rich public datasets that are easily accessible. It's not a secret that machine learning has been widely used for algorithmic trading, stock market predictions, portfolio management, and fraud detection.

Furthermore, the developments in deep learning over the years made it possible to test economic models, collect new sources of data more easily, and predict citizen behavior to help inform policymaking.

Here is the list of reliable sources of various datasets you can use for your machine learning projects.

Global Financial Development (GFD)

GFD is an extensive dataset of financial system characteristics for 214 economies around the world.  It contains annual data which has been collected since 1960.

Financial Times Markets Data

FT's up-to-date source of data on financial markets from around the world. The dataset contains information about share and stock prices, equities, currencies, bonds, and commodities performance.

Quandl

A platform with rich datasets of financial, economic, and alternative data. Quandl's data comes in two formats: Time-series (data taken over a period of time) and Tables (numerical and unsorted data types such as strings, etc.) You can download it either as a JSON or CSV file.

IMF Data

International Monetary Fund publishes data related to the IMF lending, exchange rates, and other economic and financial indicators. 

American Economic Association (AEA)

AEA's website with links to some of the most useful and popular economic data sources. It includes data on U.S macroeconomic as well as individual-level global data on income, employment, and health.

Image Datasets for Computer Vision

Now, let’s have a look at some of the best open datasets for computer vision projects. 

Some of the most popular machine learning project ideas and lab research projects are based on training visual data. Computer vision finds application in fields like medical imaging, self-driving cars, or facial recognition.

You can use an image or video datasets for a range of computer vision tasks, including image acquisition, image classification, semantic segmentation, and image analysis.

However—

To build a robust deep learning model for computer vision, you need a sizeable amount of high-quality training data.

Here's the list of open-source websites where you can access it for free.

Labelme

Labelme is an extensive dataset created by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). It contains 187,240 images, 62,197 annotated images, and 658,992 labeled objects.

ImageNet

ImageNet is one of the most popular and the largest image datasets for computer vision. It is organized according to the WordNet hierarchy. It currently holds 1,281,167 images for training and 50,000 images for validation within 1,000 categories.

Kinetics-700

Kinetics-700 is a large, high-quality video dataset of URL links to approximately 650000 Youtube video clips that cover 700 human action classes. The videos include human-object interactions, as well as human-human interactions. Kinetics dataset is great for training human action recognition model.

LSUN 

LSUN is a dataset containing around one million labeled images for each of 10 scene categories (e.g., church, dining room, etc.) and 20 object categories (e.g., bird, airplane, etc.). It aims to provide a different benchmark for large-scale scene classification and understanding.

MS COCO‍

MS COCO is a large-scale object detection, segmentation, key-point detection, and captioning open-source dataset. It contains over 200,000 labeled images. 

COIL100

COIL100 is a dataset containing 7200 color images of 100 objects (72 images per object) imaged at every angle in a 360 rotation. It was collected by the Center for Research on Intelligent Systems at Columbia University.

Visual Genome

Visual Genome is a large and detailed dataset and knowledge base with captioning of over 100.000 images.

Google’s Open Images

Google’s Open Images is a collection of over 9 million varied images with rich annotations. It contains image-level label annotations, object bounding boxes, object segmentation, and visual relationships across 6000 categories. This large image database is a great source of data for any data science project.

Youtube-8M

Youtube-8M is a vast dataset of millions of YouTube video IDs with high-quality machine-generated annotations of more than 3,800 visual entities. This dataset comes with pre-computed audio-visual features from billions of frames and audio segments.

Labeled Faces in the Wild

Labeled Faces in the Wild is a high-quality database of 13.000 face photographs designed for developing facial recognition projects. Each face has been labeled with the name of the person pictured.

Indoor Scene Recognition

Indoor Scene Recognition is a database containing 5620 images across 7 Indoor categories. There are at least 100 images per category in jpg format. 

xView

xView is a vast public dataset of overhead imagery. It contains more than 1 million object images with 60 classes from complex scenes around the world annotated using bounding boxes.

CelebFaces

CelebFaces is a large-scale dataset of more than 200K celebrity images. Each image contains 40 attribute annotations. The images cover a range of pose variations and background clutter.

Stanford Dogs Dataset

Stanford Dogs is a dataset with images of 120 breeds of dogs from around the world. It contains 20,580 images across 120 categories annotated using class labels and bounding boxes.

Places

Places is a dataset provided by MIT Computer Science and Artificial Intelligence Laboratory. There are more than 2.5 million images across 205 scene categories. Each image comes with a category label. You can use it to train deep neural networks to understand various scenes.

VisualQA

VisualQA is a new dataset containing open-ended questions about images. It includes 265,016 images (COCO and abstract scenes), at least three questions per image, and ten answers per question.

CIFAR-10

CIFAR-10 is a vast dataset containing 60000 32x32 color images in 10 classes, with 6000 images per class. It includes 50000 training images and 10000 test images.

Cityscapes Dataset

Cityscapes Dataset is a large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities. It comes with pixel-level annotations of 5 000 frames and a set of 20 000 weakly annotated frames. T

his dataset is useful in semantic segmentation and training deep neural networks to understand the urban scene.

You can check out our free dataset with 6000+ annotated X-ray lung images here.