pascal-voc

## 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.

A**nnotation 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},
}

## 帕斯卡视觉对象类（Pascal Visual Object Classes, VOC）数据集
#### 数据集概述
帕斯卡视觉对象类（VOC）数据集是计算机视觉领域广泛使用的基准测试集，专为目标检测、图像分类、语义分割以及动作分类任务设计。该数据集提供覆盖20个目标类别的全套标注图像，可供研究人员评估并对比各类算法的性能表现。
**注意**：本数据集仓库包含PASCAL-VOC的所有版本，每个文件均以年份标识。

#### 数据集结构
**图像**：本数据集共包含17.8万张图像。
**标注信息**：标注内容涵盖目标边界框、目标类别标签、分割掩码以及动作标签。
**类别**：共20个目标类别：人、自行车、汽车、摩托车、飞机、公共汽车、火车、船只、鸟类、猫、狗、马、绵羊、奶牛、大象、熊、斑马、长颈鹿以及盆栽植物。
**支持任务**
**图像分类**：从固定类别集合中为图像分配对应标签。
**目标检测**：识别图像中的目标并为其绘制边界框。
**语义分割**：为图像中的每个像素分配类别标签。
**动作分类**：识别图像中目标所执行的动作。

#### 应用场景
帕斯卡视觉对象类数据集可用于以下场景：
- 对计算机视觉算法进行基准测试与性能评估
- 训练适用于图像分类、目标检测以及分割任务的模型

#### 数据采集与标注
**数据来源**
图像采集自Flickr及其他渠道，确保样本覆盖多样且具备真实场景的代表性。
**标注流程**
标注工作由人工标注团队完成，每张图像的标注内容包括：
- 用于目标检测的边界框
- 每个目标的类别标签
- 用于语义分割的逐像素分割掩码
- 标注图像内目标所执行动作的标签

#### 许可协议
帕斯卡视觉对象类数据集采用知识共享署名2.5许可（Creative Commons Attribution 2.5 License）协议发布。用户可自由共享、改编并使用该数据集，但需提供恰当的署名引用。

#### 引用说明
若您在研究中使用帕斯卡视觉对象类数据集，请引用以下论文：

@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},
}

搜集汇总

背景与挑战

背景概述

Pascal VOC数据集是一个广泛使用的计算机视觉基准数据集，主要用于对象检测、图像分类、语义分割和动作分类任务。它包含178k张图像，覆盖20个常见对象类别，并提供详细的注释（如边界框和分割掩码），图像来源于Flickr等真实场景，由人工标注，适用于算法评估和模型训练。