openimages/dataset

Features

• Diverse Visual Datasets - Provides a broad collection of global images featuring diverse objects and people to improve model generalization.
• Image Labeling - Provides millions of labeled images with bounding boxes and point locations to generate ground truth for computer vision.
• Visual Object Grounding - Maps visual entities to geometric coordinates using bounding boxes and pixel-level masks for object localization.
• Computer Vision Benchmarks - Serves as a standardized benchmark for computing precision and recall in object detection and classification models.
• Instance Segmentation Engines - Provides pixel-level masks that delineate exact object boundaries to separate individual instances from the background.
• Object Detection - Locates object instances using precise spatial bounding boxes across hundreds of classes.
• Segmentation Object Management - Delineates exact pixel-level boundaries of individual objects using high-resolution masks.
• Computer Vision Training - Provides a benchmark dataset and evaluation scripts for training supervised machine learning models for visual recognition.
• Dense Visual Annotations - Provides detailed labels including bounding boxes, segmentation masks, and point-level annotations for image subsets.
• Image Annotation Datasets - Provides an extensive library of bounding boxes, segmentation masks, and labels to teach models scene perception.
• Multimedia Content Analyzers - Processes multimodal data, including synchronized voice and text, to provide detailed descriptions of visual content.
• Image Segmentation Datasets - Provides detailed pixel masks and point-level annotations for high-precision instance and semantic segmentation.
• Multimodal Narrative Datasets - Provides synchronized voice, text, and mouse traces paired with image regions to train narrative understanding models.
• Multimodal Narrative Synchronizations - Pairs visual regions with synchronized voice, text, and mouse traces to link natural language to specific image areas.
• Multimodal Visual Understanding - Integrates visual and language data, linking voice traces and narratives to image regions for complex reasoning.
• Coordinate-Based Spatial Mappings - Represents visual entities as geometric coordinates using bounding boxes and pixel masks for spatial mapping.
• Visual Relationship Modeling - Maps the relationships between different objects and their attributes within images to support complex scene understanding.
• Visual Relationship Triplets - Represents scene interactions by linking two objects and their specific interaction or an object and its attribute.
• Image Annotation - Provides a comprehensive repository of bounding boxes, masks, and relationship labels across thousands of classes.
• Media Metadata Indexes - Indexes images against a structured hierarchy of categories and attributes to enable efficient dataset subset filtering.
• Visual Relationship Triplets - Identifies triplets consisting of two objects and their interaction or an object and its physical property.
• Dataset Category Filters - Provides a system to filter and retrieve image subsets based on a taxonomy of thousands of human-verified categories.
• AI Ethics and Fairness - Includes demographic attributes and exhaustive annotations to evaluate bias and fairness in machine learning models.
• Dataset Splitting Utilities - Includes utilities for dividing large-scale visual data into training, validation, and test sets based on label distribution.
• Dataset Subset Extractions - Provides tools and scripts to extract specific subsets of the dataset based on classes, attributes, or metadata.
• Pre-trained Model Zoos - Provides model checkpoints for image classification and object detection to facilitate immediate inference or fine-tuning.
• Model Performance Evaluators - Enables quantification of model accuracy and reliability via mean Average Precision and precision-recall curves.
• Vision Model Training - Supplies large-scale visual data including bounding boxes and instance segmentations for training supervised models.
• Scene Text Recognition - Extracts hierarchical text annotations from images of natural scenes and documents at the word, line, and paragraph levels.
• Point-Level Classifications - Identifies and categorizes specific pixels or regions using dense point-level annotations across thousands of classes.
• Visual Relationship Datasets - Includes object-attribute triplets that map interactions and relationships between different visual entities in a scene.
• Zero-Shot Segmentations - Assigns semantic labels to pixel coordinates to facilitate zero-shot or few-shot semantic segmentation.
• Bias and Fairness - Includes demographic attributes and exhaustive annotations to help detect and mitigate biases in multimodal representations.
• Multimodal Training Datasets - Pairs images with synchronized voice, text, and mouse traces to support multimodal narrative understanding.
• Point-Level Semantic Annotations - Assigns discrete category labels to individual pixel coordinates to enable dense classification and zero-shot segmentation.
• Hierarchical Text Annotations - Organizes optical character recognition data into nested levels of words, lines, and paragraphs for structured analysis.
• Image Classifiers - Provides human-verified positive and negative labels across thousands of classes for image categorization.
• Dataset Filtering - Allows isolating specific data slices based on object classes, annotation types, or dataset splits.
• Batch Image Downloads - Enables the batch retrieval of raw images, thumbnails, and associated metadata like rotation and licensing.
• Computer Vision Datasets - Large-scale image collection annotated with thousands of object categories.

Star-Verlauf