Vehicles-COCO2
Vehicles-COCO2
Datasets
Vehicles-COCO2
File
Vehicles-COCO2
Use Case
Vehicles-COCO2
Description
Explore the Open Images Subset with annotations converted to COCO format. Featuring 6 object classes, this dataset is ideal for object detection research, AI training, and educational projects.
Description:
The Open Images Subset (COCO Format) is a curated dataset featuring 6 object classes with annotations converted to the COCO format. Derived from the Open Images dataset, it supports object detection research, AI model training, and educational projects with high-quality, standardized annotations.
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The Open Images Subset (COCO Format) is a carefully selected collection of data from the Open Images dataset. It is specifically designed for object detection tasks. With annotations converted to the popular COCO format, this subset is perfect for seamless use in machine learning models and tools.
Key Features
COCO Format Annotations:
- The object detection annotations from Open Images have been converted to the widely accepted COCO format. This ensures compatibility with popular AI frameworks like YOLO, TensorFlow, and Detectron2.
Six Featured Classes:
- The subset focuses on six distinct object classes, making it useful for targeted object detection projects.
High-Quality Source:
- Sourced from the Open Images dataset, the subset maintains high image quality and diverse data for reliable model training.
Applications
- Object Detection Research:
- Use this dataset to test and improve object detection algorithms with standardized COCO annotations.
- AI Model Training:
- Train machine learning models more effectively with a high-quality dataset in COCO format.
- Education and Learning:
- Provide a practical resource for students and researchers working on object detection and AI projects.
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