COCO
The COCO (Common Objects in Context) dataset is a foundational tool for the computer vision community, designed to facilitate object detection, segmentation, and captioning tasks. It includes over 330,000 images, more than 200,000 of which are labeled, featuring complex scenes with multiple objects in natural contexts. COCO provides rich annotations, such as object bounding boxes, segmentation masks, and detailed image captions. This dataset supports a broad range of applications and research in image understanding and has spurred advancements in AI by serving as a benchmark for annual challenges that push the limits of object recognition and image captioning technologies.
Description:
- Content: Features over 330,000 images with more than 200,000 labeled.
- Categories: Includes 80 object categories and more than 1.5 million object instances.
- Annotations: Provides rich annotations such as object segmentation, bounding boxes, and keypoint detection for each object.
- Variety of Tasks: Supports a wide range of vision tasks including object detection, segmentation, and captioning.
- Purpose: Designed to advance the state-of-the-art in object recognition by placing objects in the context of their natural environment, with complex scenes and multiple objects per image.
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.