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Pascal VOC
Dataset Summary
The Pascal Visual Object Classes (VOC) dataset is a widely used benchmark in the field of computer vision. It is designed for object detection, image classification, semantic segmentation, and action classification tasks. The dataset provides a comprehensive set of annotated images covering 20 object classes, allowing researchers to evaluate and compare the performance of various algorithms. Note: This dataset repository contains all editions of PASCAL-VOC, each file is identified with the year.
Dataset Structure
Images: The dataset contains 178k images. Annotations: Annotations include object bounding boxes, object class labels, segmentation masks, and action labels. Classes: 20 object classes: person, bicycle, car, motorbike, aeroplane, bus, train, boat, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, and potted plant. Supported Tasks Image Classification: Assigning a label to an image from a fixed set of categories. Object Detection: Identifying objects within an image and drawing bounding boxes around them. Semantic Segmentation: Assigning a class label to each pixel in the image. Action Classification: Identifying the action being performed in the image.
Applications
The Pascal VOC dataset is used for:
- Benchmarking and evaluating computer vision algorithms.
- Training models for image classification, object detection, and segmentation tasks.
Data Collection and Annotation
Data Sources The images were collected from Flickr and other sources, ensuring a diverse and representative sample of real-world scenes.
Annotation Process Annotations were carried out by a team of human annotators. Each image is labeled with:
- Bounding boxes for object detection.
- Class labels for each object.
- Pixel-wise segmentation masks for semantic segmentation.
- Action labels indicating the action performed by the objects in the image.
License
The Pascal VOC dataset is released under the Creative Commons Attribution 2.5 License. Users are free to share, adapt, and use the dataset, provided appropriate credit is given.
Citation
If you use the Pascal VOC dataset in your research, please cite the following paper:
@article{Everingham10,
author = {Mark Everingham and
Luc Gool and
Christopher K. I. Williams and
John Winn and
Andrew Zisserman},
title = {The Pascal Visual Object Classes (VOC) Challenge},
journal = {International Journal of Computer Vision},
volume = {88},
number = {2},
year = {2010},
pages = {303-338},
}
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