Open Images
Open Images is a diverse and large-scale dataset designed for computer vision research, hosted by Google. It contains approximately 9 million images annotated with labels spanning thousands of object categories. The dataset is known for its rich annotations, including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives that provide textual descriptions of image content. Open Images supports a variety of computer vision tasks such as object detection, visual relationship detection, and segmentation. It is particularly useful for training and evaluating models due to its wide variety of annotated objects and complex scenes, making it a valuable resource for advancing image recognition technologies.
Description:
- Content: Comprises over 9 million images annotated with labels.
- Categories: Features a diverse range of approximately 6000 categories.
- Annotations: Rich annotations including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.
- Scale and Diversity: One of the largest and most diverse datasets available, with images collected from a variety of sources and scenarios, intended to represent a broad spectrum of everyday scenes.
- Purpose: Serves multiple computer vision tasks such as object detection, visual relationship detection, and instance segmentation, supporting the development of more robust and versatile AI models.
Dataset for Image Classification
The field of computer vision has witnessed remarkable progress in recent years, largely driven by the availability of large-scale datasets for image classification tasks. These datasets play a pivotal role in training and evaluating machine learning models, enabling them to recognize and categorize visual content with increasing accuracy.
In this article, we will discuss some of the famous datasets used for image classification.