What Are Machine Learning Datasets?
Machine learning datasets are collections of data used to train, test, and validate machine learning models. These datasets can include images, text, audio, video, or structured data. They are fundamental to developing algorithms, enabling machines to learn patterns, make predictions, and improve through experience.
Datasets vary in size, complexity, and type, depending on the specific needs of a project. A dataset must be representative of real-world scenarios to effectively train models. This means adequately capturing the variety and variability in data that the model will encounter post-deployment.
Why Are Machine Learning Datasets Important?
Machine learning datasets play a critical role in the accuracy and efficiency of AI models. A well-curated dataset ensures that the model can generalize well to new, unseen data. This is essential for creating robust models that perform consistently across different conditions and environments.
The importance of datasets extends to model evaluation. Test and validation datasets help developers understand a model’s limitations and strengths, ensuring that models operate reliably before they are deployed in critical applications like healthcare, finance, and autonomous driving.
Open Dataset Aggregators
There are several websites and organizations that collect machine learning datasets and allow researchers to easily search and filter to find a dataset of interest. Here are some of the prominent aggregators.
In the following sections, we provide brief information about the most prominent datasets so you can access them directly.
1. Kaggle
Kaggle is a platform that hosts machine learning competitions and provides datasets for a wide range of applications. It’s not only a resource for data but also a community where data scientists share insights and solutions. Kaggle datasets vary from historical crime rates to detailed financial data, providing both beginners and experts with valuable resources.
Kaggle also offers “Kernels”, a feature allowing users to run scripts on provided datasets directly in the browser, providing immediate hands-on experience. This feature is particularly helpful for new learners to start experimenting without any setup.
Direct link: Kaggle datasets
2. OpenML
OpenML is a public repository for machine learning data and experiments. It is designed to share data in a way that machines can easily access and analyze them. OpenML integrates seamlessly with popular machine learning frameworks, making it a versatile tool for researchers and practitioners.
The platform encourages open collaboration by allowing users to upload and categorize their own datasets. This behavior fosters a growing library of machine learning resources accessible to the global data science community.
Direct link: OpenML
3. UCI Machine Learning Repository
The UCI Machine Learning Repository is a database of over 600 datasets specifically designed for statistical and machine learning research. It includes a diverse collection of data that spans various domains such as finance, biology, social sciences, and more.
Since its inception in 1987, the UCI repository has been a fundamental resource for machine learning students and researchers needing datasets for projects and experiments.
Direct link: UCI Machine Learning Repository
4. Google Dataset Search
Google Dataset Search is a tool that helps researchers locate online data that is freely available for use. The tool draws from various sources including publishers’ sites, digital libraries, and personal web pages. It has become increasingly useful for finding datasets across disciplines and formats.
The user-friendly interface allows for filtering results based on the types of datasets required, making it simpler to find the right kind of data.
Direct link: Google Dataset Search
5. Papers with Code
Papers with Code is a free resource that not only provides access to datasets but also links to the latest research papers and their corresponding code repositories. This bridges the gap between theoretical research and practical applications.
Each paper featured on the platform is associated with a dataset used for the experiments, making it easier for users to replicate studies and explore variations of existing models, thereby enhancing transparency and reproducibility in machine learning research.
Direct link: Papers with Code
Top 21 Machine Learning Datasets
The summary table below provides the essential details about some of the world’s most important machine learning datasets. Below we provide more detail about each dataset.
| Dataset Category | Dataset Name | Purpose | Type of Data | Number of Data Items |
| Computer Vision | ImageNet | Visual object recognition | Images | Over 14 million images |
| Computer Vision | COCO (Common Objects in Context) | Object detection, segmentation, and captioning | Images | Over 330,000 images |
| Computer Vision | CIFAR-10 | Benchmarking algorithms | Images | 60,000 images |
| Computer Vision | Labelme | Training models for recognition and segmentation tasks | Annotated images | Over 110,000 polygons across 50,000 images |
| Computer Vision | CelebA | Facial recognition technologies | Annotated celebrity images | Over 200,000 images |
| Computer Vision | FFHQ (Flickr-Faces-HQ) | Training sophisticated face-recognition models | High-resolution images | 70,000 images |
| Computer Vision | Labeled Faces in the Wild (LFW) | Face verification | Annotated faces | Over 13,000 images |
| Computer Vision | LSUN (Large-scale Scene Understanding) | Scene understanding | Labeled images | Over 59 million images |
| Natural Language Processing | SNLI (Stanford Natural Language Inference) | Training and evaluating natural language understanding systems | Text (sentence pairs) | 570,000 pairs |
| Natural Language Processing | Common Crawl | Training and evaluating natural language processing models | Web documents | Over 250 billion web pages |
| Natural Language Processing | Multi-Domain Sentiment Dataset (MDS) | Sentiment analysis | Product reviews | Not disclosed |
| Natural Language Processing | Wikipedia Links Data | Entity recognition and linking | Annotated documents | Over 10 million pages |
| Audio | Common Voice | Inclusive voice recognition | Recorded voices | Over 9,000 hours of recordings |
| Audio | AudioSet | Audio event recognition | Audio recordings | 2 million snippets |
| Audio | LibriSpeech | Speech recognition | English speech | 1,000 hours |
| Audio | VoxForge | Voice recognition in multiple languages | Transcribed speech | Not disclosed |
| Audio | Free Music Archive (FMA) | Music and audio analysis | Music tracks | Over 100,000 tracks |
| Public Sector | USA.gov Data | Research and development across various sectors | Multiple data types | Over 290,000 datasets |
| Public Sector | EU Open Data Portal | Driving innovation and research in the EU | Multiple data types | Over 15,000 datasets |
| Public Sector | Data.gov.uk | Supporting transparency and innovation in the UK | Public sector data | Over 47,000 datasets |
| Public Sector | US Healthcare Data | Developing AI applications in healthcare | Healthcare-related data | Not disclosed |
Image Datasets for Computer Vision
1. ImageNet
Type of data: Images
Number of data items: 14 million
Link to dataset: https://www.image-net.org/
ImageNet is a foundational dataset designed for use in visual object recognition software research. Over 14 million images have been hand-annotated to indicate what objects are pictured, and in at least one million of the images, bounding boxes are also provided. This database has become a benchmark in AI research for developing more advanced image recognition technologies.
Its broad and diverse set of images and associated annotations are used widely in training machine learning models that require categorization of diverse sets of imagery.
2. COCO (Common Objects in Context)
Type of data: Images
Number of data items: +330,000
Link to dataset: https://cocodataset.org/#download
COCO is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage the development of object segmentation models capable of understanding where one object stops, and another starts. COCO also provides localization, categorization, and segmentation of objects within each image, making it versatile for various computer vision tasks.
This dataset is useful for training AI to understand not just what objects are in a picture, but the context of those objects, which is essential for systems that interact with their environment, such as autonomous vehicles or interactive robots.
3. CIFAR-10
Type of data: Images
Number of data items: 60,000
Link to dataset: https://www.cs.toronto.edu/~kriz/cifar.html
CIFAR-10 consists of 60,000 32×32 color images in 10 classes, with 6,000 images per class. It is widely used by the machine learning community for benchmarking algorithms against standard datasets to see how well they perform in categorizing small images.
The simplicity and fixed size of these images make CIFAR-10 a useful dataset for testing and optimizing algorithms, especially when computational resources are limited.
4. CelebA
Type of data: Images
Number of data items: 202,599
Link to dataset: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
The CelebA dataset consists of over 200,000 celebrity images, each with 40 attribute annotations that include points on the face such as eyes, nose, and mouth, along with other non-geometric attributes. These annotations make it possible for researchers to train more specialized models in facial recognition technologies.
The specific focus on annotated facial features sets CelebA apart as an invaluable research tool for projects that require detailed understanding of facial characteristics, such as identifying emotions or improving interactive entertainment systems.
5. FFHQ
Type of data: Images
Number of data items: 70,000
Link to dataset: https://github.com/NVlabs/ffhq-dataset
The Flickr-Faces-HQ (FFHQ) dataset contains 70,000 high-resolution images of human faces, carefully compiled from Flickr and standardized to a 1024×1024 format. These images represent a diverse demographic, representing different ages, ethnicities, and backgrounds, providing a comprehensive dataset for training face-recognition models.
FFHQ’s high resolution and diversity are critical for developing more accurate and fair AI systems that need to operate reliably across diverse populations.
6. Labeled Faces in the Wild (LFW)
Type of data: Images
Number of data items: 13,233
Link to dataset: https://vis-www.cs.umass.edu/lfw/
Labeled Faces in the Wild is a public benchmark for face verification, consisting of over 13,000 images of faces collected from the web. Each face has been labeled with the person’s name, making it one of the most challenging datasets due to the variations in pose, lighting, and expressions.
The difficulty and real-world applicability of LFW make it an essential tool for developers looking to test and enhance face recognition systems under challenging conditions, helping to ensure that these technologies are robust and reliable.
7. LSUN
Type of data: Images
Number of data items: 59 million
Link to dataset: https://github.com/fyu/lsun
Large-scale Scene Understanding (LSUN) dataset challenges machine learning models to understand large-scale scenes and has annotations for each scene type in categories such as bedroom, kitchen, church, and more. It contains millions of labeled images used for deep learning and scene understanding tasks.
By focusing on various indoor and urban scenes, LSUN helps AI systems learn to navigate and interpret complex environments effectively, which is vital for applications in autonomous vehicles, augmented reality, and more.
8. Labelme
Type of data: Polygons
Number of data items: 111,490
Link to dataset: https://github.com/labelmeai/labelme
Labelme is a graphical image annotation tool that also comes with an extensive and growing database of images that have been manually annotated by the application’s user community. These annotations create datasets that can be used for a variety of machine learning projects, especially in training models for recognition and segmentation tasks.
The interactive nature of Labelme allows users to contribute to the dataset, making it richer and more diverse. This community-driven approach continuously adds highly specific datasets that can be used for computer vision projects.
Natural Language Processing Datasets
9. Stanford Natural Language Inference (SNLI) Corpus
Type of data: Sentence pairs
Number of data items: 570,000
Link to dataset: https://nlp.stanford.edu/projects/snli/
Stanford Natural Language Inference Corpus is a collection of 570,000 human-written English sentence pairs manually labeled for balanced classification. This dataset aids in training and evaluating natural language understanding systems regarding inference capabilities, such as determining if one sentence logically follows another.
Understanding the fundamentals of human language inference is vital for improving how machines interact with human language, enhancing capabilities in chatbots, translation services, and other applications where understanding context and subtlety are essential.
10. Common Crawl
Type of data: Web pages
Number of data items: 150 billion
Link to dataset: https://commoncrawl.org/
Common Crawl produces large-scale web crawl data collected over seventeen years, which is a valuable resource for training and evaluating natural language processing models. It currently has over 250 billion pages. The dataset provides a comprehensive snapshot of the Internet, offering over a petabyte of data across billions of web pages.
This dataset is particularly useful for projects that require a broad understanding of language usage and web documents and serves as a fundamental resource for research and innovation in machine learning, particularly in understanding and generating human-like text.
11. Multi-Domain Sentiment Dataset (MDS)
Type of data: Reviews
Link to dataset: https://www.cs.jhu.edu/~mdredze/datasets/sentiment/
Multi-Domain Sentiment Dataset contains product reviews from Amazon across four product types (kitchen, books, electronics, and DVDs). Each review is labeled with its sentiment, making MDS crucial for training and testing sentiment analysis models.
The MDS dataset allows AI systems to learn how different words and phrases signal positive or negative sentiments, which can be applied in systems like customer service chatbots to detect and respond to user sentiments effectively.
12. Wikipedia Links Data
Type of data: Documents
Number of data items: +10 million
Link to dataset: https://www.iesl.cs.umass.edu/data/data-wiki-links
Wikipedia Links Data consists of a comprehensive set of documents from Wikipedia featuring annotated mention spans to associated entity pages. It covers 10,893,248 pages with over 40 million mentions. This dataset is crucial for developing and training models that need to recognize entities and link them to a broader knowledge base, essential for tasks in information extraction and retrieval.
By using a dataset built around links, AI models can better understand context, relevance, and relationships between entities, which enhances their ability to synthesize information and answer questions more effectively.
Audio Speech and Music Datasets
13. Common Voice
Type of data: Voice recordings
Number of data items: 9,273 hours
Link to dataset: https://commonvoice.mozilla.org/en
Common Voice is an initiative by Mozilla that aims to make voice recognition technology more inclusive through the creation of a publicly available dataset of recorded voices in various languages. Contributors worldwide can donate their voice samples along with, crucially, demographic data that ensures diversity in the dataset.
The broad and inclusive nature of Common Voice ensures that voice recognition technologies built using it are more effective across different accents, languages, and dialects, enhancing accessibility and usability in applications like voice-activated assistants.
14. AudioSet
Type of data: Sound clips
Number of data items: 2 million
Link to dataset: https://www.cs.cmu.edu/~alnu/tlwled/audioset.htm
AudioSet consists of an extensive collection of audio recordings that capture a wide range of sounds from human speech, animal sounds, musical instruments, and other sound events. The dataset includes an ontology of 632 audio event classes and millions of human-labeled 10-second sound snippets drawn from YouTube videos.
This breadth makes AudioSet highly valuable for training and benchmarking algorithms in the area of audio context recognition, which is critical for applications such as automated content moderation or environmental noise analysis.
15. LibriSpeech
Type of data: Recordings
Number of data items: 1,000 hours
Link to dataset: https://www.openslr.org/12
LibriSpeech is a dataset of approximately 1,000 hours of English speech derived from audiobooks in the public domain. It is commonly used for training and evaluating speech recognition systems. The dataset is unique in its focus on reading speech, which presents challenges different from conversational speech.
With a sizable volume and variety of speaking styles, LibriSpeech serves as a solid benchmark for assessing the performance of speech recognition models under varying acoustic conditions.
16. VoxForge
Type of data: Speech transcripts
Link to dataset: https://www.voxforge.org/
VoxForge is a free speech corpus collected and maintained by volunteers, aimed at providing transcribed speech for use in voice recognition software across six languages (English, French, Spanish, German, Italian, and Russian). The community-driven approach helps in creating a diverse and extensive voice dataset.
By encouraging user participation in multiple languages, VoxForge enriches the availability of speech data necessary for global speech recognition applications.
17. Free Music Archive (FMA)
Type of data: Music tracks
Number of data items: 106,574
Link to dataset: https://freemusicarchive.org/
Free Music Archive (FMA) offers a substantial collection of high-quality, legal audio downloads. The database is rich with well-annotated metadata including genres, artists, and albums, making it a valuable resource for any machine learning project involving music and audio analysis.
With its diverse range of audio files, FMA serves as a practical dataset for algorithms that require robustness across varied musical styles and formats, aiding development in areas such as music recommendation and classification systems.
Public Government Datasets for Machine Learning
18. USA.gov Data
Type of data: Datasets
Number of data items: 296,619
Link to dataset: https://data.gov/
USA.gov offers extensive datasets ranging from agriculture, health to consumer data that are publicly available through the U.S. government’s open data portal. These datasets are used in research and development across various sectors, enhancing transparency, accountability, and public engagement.
The availability of these datasets allows researchers and developers to tackle problems specific to public welfare and policy planning, making machine learning tools more accessible and applicable to societal needs.
19. EU Open Data Portal
Type of data: Datasets
Number of data items: 15,399
Link to dataset: https://data.europa.eu/en
The EU Open Data Portal aggregates a diverse range of data from different departments of the European Union. It provides datasets on topics like agriculture, finance, science, and environment. These datasets are driving innovation and research within the EU.
By making these datasets freely available, the EU Open Data Portal facilitates the development of solutions that cater to the complexities of multiple countries, languages, and legal frameworks, promoting cross-border collaboration in machine learning projects.
20. Data.gov.uk
Type of data: Datasets
Number of data items: 47,000
Link to dataset: https://www.data.gov.uk/
Data.gov.uk provides public sector data in the UK for free use and re-use. It hosts data from national and local government organizations and includes topics like education, public safety, and employment.
The datasets are continuously maintained and updated, ensuring that users have access to the most relevant and current information for research and application development.
21. US Healthcare Data
Type of data: Healthcare-related data
Link to dataset: https://healthdata.gov/
US Healthcare Data is managed by various federal agencies providing datasets related to healthcare services, health outcomes, and insurance. These datasets are critical for developing AI applications that aim to improve patient care and healthcare administration.
Access to such specific and high-quality data enables machine learning practitioners to create more accurate models that can predict patient outcomes, personalize treatments, and manage healthcare resources.
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