Skip to content

VOC Dataset

The PASCAL VOC (Visual Object Classes) dataset is a well-known object detection, segmentation, and classification dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and classification tasks.

Key Features

  • VOC dataset includes two main challenges: VOC2007 and VOC2012.
  • The dataset comprises 20 object categories, including common objects like cars, bicycles, and animals, as well as more specific categories such as boats, sofas, and dining tables.
  • Annotations include object bounding boxes and class labels for object detection and classification tasks, and segmentation masks for the segmentation tasks.
  • VOC provides standardized evaluation metrics like mean Average Precision (mAP) for object detection and classification, making it suitable for comparing model performance.

Dataset Structure

The VOC dataset is split into three subsets:

  1. Train: This subset contains images for training object detection, segmentation, and classification models.
  2. Validation: This subset has images used for validation purposes during model training.
  3. Test: This subset consists of images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the PASCAL VOC evaluation server for performance evaluation.


The VOC dataset is widely used for training and evaluating deep learning models in object detection (such as YOLO, Faster R-CNN, and SSD), instance segmentation (such as Mask R-CNN), and image classification. The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners.

Dataset YAML

A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the VOC dataset, the VOC.yaml file is maintained at


# Ultralytics YOLO 🚀, AGPL-3.0 license
# PASCAL VOC dataset by University of Oxford
# Example usage: yolo train data=VOC.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── VOC  ← downloads here (2.8 GB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/VOC
train: # train images (relative to 'path')  16551 images
  - images/train2012
  - images/train2007
  - images/val2012
  - images/val2007
val: # val images (relative to 'path')  4952 images
  - images/test2007
test: # test images (optional)
  - images/test2007

# Classes
  0: aeroplane
  1: bicycle
  2: bird
  3: boat
  4: bottle
  5: bus
  6: car
  7: cat
  8: chair
  9: cow
  10: diningtable
  11: dog
  12: horse
  13: motorbike
  14: person
  15: pottedplant
  16: sheep
  17: sofa
  18: train
  19: tvmonitor

# Download script/URL (optional) ---------------------------------------------------------------------------------------
download: |
  import xml.etree.ElementTree as ET

  from tqdm import tqdm
  from ultralytics.yolo.utils.downloads import download
  from pathlib import Path

  def convert_label(path, lb_path, year, image_id):
      def convert_box(size, box):
          dw, dh = 1. / size[0], 1. / size[1]
          x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
          return x * dw, y * dh, w * dw, h * dh

      in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
      out_file = open(lb_path, 'w')
      tree = ET.parse(in_file)
      root = tree.getroot()
      size = root.find('size')
      w = int(size.find('width').text)
      h = int(size.find('height').text)

      names = list(yaml['names'].values())  # names list
      for obj in root.iter('object'):
          cls = obj.find('name').text
          if cls in names and int(obj.find('difficult').text) != 1:
              xmlbox = obj.find('bndbox')
              bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
              cls_id = names.index(cls)  # class id
              out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')

  # Download
  dir = Path(yaml['path'])  # dataset root dir
  url = ''
  urls = [f'{url}',  # 446MB, 5012 images
          f'{url}',  # 438MB, 4953 images
          f'{url}']  # 1.95GB, 17126 images
  download(urls, dir=dir / 'images', curl=True, threads=3)

  # Convert
  path = dir / 'images/VOCdevkit'
  for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
      imgs_path = dir / 'images' / f'{image_set}{year}'
      lbs_path = dir / 'labels' / f'{image_set}{year}'
      imgs_path.mkdir(exist_ok=True, parents=True)
      lbs_path.mkdir(exist_ok=True, parents=True)

      with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
          image_ids =
      for id in tqdm(image_ids, desc=f'{image_set}{year}'):
          f = path / f'VOC{year}/JPEGImages/{id}.jpg'  # old img path
          lb_path = (lbs_path /'.txt')  # new label path
          f.rename(imgs_path /  # move image
          convert_label(path, lb_path, year, id)  # convert labels to YOLO format


To train a YOLOv8n model on the VOC dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.

Train Example

from ultralytics import YOLO

# Load a model
model = YOLO('')  # load a pretrained model (recommended for training)

# Train the model
model.train(data='VOC.yaml', epochs=100, imgsz=640)
# Start training from
a pretrained *.pt model
yolo detect train data=VOC.yaml epochs=100 imgsz=640

Sample Images and Annotations

The VOC dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations:

Dataset sample image

  • Mosaiced Image: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.

The example showcases the variety and complexity of the images in the VOC dataset and the benefits of using mosaicing during the training process.

Citations and Acknowledgments

If you use the VOC dataset in your research or development work, please cite the following paper:

      title={The PASCAL Visual Object Classes (VOC) Challenge}, 
      author={Mark Everingham and Luc Van Gool and Christopher K. I. Williams and John Winn and Andrew Zisserman},

We would like to acknowledge the PASCAL VOC Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the VOC dataset and its creators, visit the PASCAL VOC dataset website.

Created 2023-05-08, Updated 2023-05-17
Authors: Glenn Jocher (3)