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Searching for a suitable dataset for your machine learning project can be time consuming.
As such, we have compiled a list of the top 10 open-source datasets spanning image recognition to natural language processing that will save you time and help you get started.
Whether you are a beginner or professional this diverse list of datasets will empower your machine learning advancements.
Open-source datasets are publicly available datasets that are shared with no restrictions on usage or distribution. These datasets are typically released under open licenses, which allows researchers, developers, and enthusiasts to freely access, utilize, and contribute to the data. This fosters collaboration, innovation, and the advancement of research and development in fields such as machine learning, computer vision, and natural language processing.
Open-source datasets are valuable resources for machine learning and computer vision projects:
These benefits support researchers, expedite development, and foster creativity in the fields of machine learning and computer vision.
SA-1B dataset consists of 11 million varied and high-resolution images along with 1.1 billion pixel-level annotations, making it suitable for training and evaluating advanced computer vision models.
This dataset was collected by Meta for their Segment Anything project and the images in the dataset are automatically generated by the Segment Anything Model (SAM). With the SA-1B dataset, researchers and developers can explore a wide range of applications in computer vision, including scene understanding, object recognition, instance segmentation, and image parsing. The dataset's rich annotations allow for detailed analysis and modeling of object boundaries, semantic regions, and fine-grained object attributes.
The Visual Question Answering (VQA) dataset consists of 260,000 images that depict abstract scenes from COCO, multiple questions and answers per image, and an automatic evaluation metric, challenging machine learning models to comprehend images and answer open-ended questions by combining vision, language, and common knowledge.
This comprehensive dataset offers a wide range of applications, including image understanding, question generation, and multi-modal reasoning. With its large-scale and diverse nature, the VisualQA dataset provides a rich source of training and evaluation data for developing sophisticated AI algorithms. Researchers can leverage this dataset to enhance the capabilities of visual question-answering systems, allowing them to interpret images and accurately respond to human-generated questions.
The ADE20K dataset provides over 20,000 diverse and densely annotated images which serves as a benchmark for developing computer vision models for semantic segmentation.
The dataset offers high-quality annotations and covers both object and stuff classes, providing a comprehensive representation of scenes. It was created by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and is available for research and non-commercial use. The significance of the ADE20K dataset lies in its capacity to drive research in scene understanding, object recognition, and image parsing, leading to advancements in computer vision techniques and benefiting diverse applications like autonomous driving, object detection, and image analysis.
YouTube-8M is a large-scale video dataset containing 7 million YouTube videos annotated with a wide range of visual and audio labels for various machine learning tasks.
YouTube-8M is a valuable resource for machine learning tasks, allowing researchers and developers to train and assess models for video understanding, action recognition, video summarization, and visual feature extraction.
Google's Open Images is a publicly accessible dataset that provides 8 million labeled images, offering a valuable resource for various computer vision tasks and research.
Google's Open Images is used for various purposes such as object detection, image classification, and visual recognition. The dataset's importance lies in its comprehensive coverage and large-scale nature, enabling researchers and developers to train and evaluate advanced AI models.
MS COCO (Common Objects in Context) is a widely used large-scale dataset that contains 330,000 diverse images with rich annotations for tasks like object detection, segmentation, and captioning.
MS COCO (Common Objects in Context) is specifically designed for object detection, segmentation, and captioning tasks, offering detailed annotations for a wide range of objects in various real-world contexts. The dataset has become a benchmark for evaluating and advancing state-of-the-art models in visual understanding and has played a significant role in driving progress in the field of computer vision.
The CT Medical Image dataset is a small sample extracted from the cancer imaging archive, comprised of the middle slice of CT images that meet specific criteria regarding age, modality, and contrast tags.
The dataset is designed to train models to recognize image textures, statistical patterns, and highly correlated features associated with these characteristics. This can enable the development of straightforward tools for automatically classifying misclassified images and identifying outliers that may indicate suspicious cases, inaccurate measurements, or inadequately calibrated machines.
The Aff-Wild dataset consists of 564 videos of around 2.8 million frames with 554 subjects and is designed for the task of emotion recognition using facial images.
Aff-Wild provides a diverse collection of facial images captured under various conditions, including different head poses, illumination conditions, and occlusions. The dataset serves as a valuable resource for developing and evaluating algorithms and models for emotion recognition, gesture recognition, and action unit detection in computer vision.
DensePose-COCO consists of 50,000 images with dense human pose estimation annotations for each person in the COCO dataset, enabling a detailed understanding of the human body's pose and shape.
DensePose-COCO is an extension of the COCO dataset that provides dense human pose annotations, allowing precise mapping of body landmarks and segmentation. It serves as a benchmark for pose estimation and shapes understanding in computer vision research.
The BDD100K dataset is a large-scale diverse driving video dataset that contains over 100,000 videos.
The BDD100K dataset is a valuable asset for advancing autonomous driving research, computer vision algorithms, and robotics. It plays a crucial role in improving perception systems and facilitating the development of intelligent transportation systems. With its diverse and comprehensive annotations, BDD100K is utilized for various computer vision tasks, including object detection, instance segmentation, and scene understanding, empowering researchers and developers to push the boundaries of computer vision technology in autonomous driving and transportation.
Encord enables easy one-command downloads of these open-source datasets and provides the flexibility to explore, analyze, and curate datasets tailored to your project's specific requirements. By utilizing the platform, you will streamline your data collection process and enhance the efficiency and effectiveness of your machine learning workflows.
To use open-source datasets in the Encord platform, simply follow these steps:
python3.9 -m venv ea-venv source ea-venv/bin/activate # within venv pip install encord-active
# within venv encord-active download
With those simple steps, you now have your dataset!
With Encord, you can accelerate the image and video labeling process of your machine learning project while also facilitating the analysis of your models.
Encord annotate empowers annotators with a diverse set of annotation types tailored for various computer vision applications. Meanwhile, Encord Active equips machine learning practitioners with a comprehensive toolset for data analysis, labeling, and assessing model quality.
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