---
pretty_name: PASCAL VOC 2012
license: other
license_name: pascal-voc
license_link: https://host.robots.ox.ac.uk/pascal/VOC/voc2012/
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- image-segmentation
- object-detection
tags:
- pascal-voc
- voc2012
- semantic-segmentation
- sbd
dataset_info:
- config_name: detection
  features:
  - name: id
    dtype: string
  - name: image
    dtype: image
  - name: width
    dtype: int32
  - name: height
    dtype: int32
  - name: objects
    struct:
    - name: label
      list:
        class_label:
          names:
            '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
    - name: bbox
      list:
        list: float32
        length: 4
    - name: difficult
      list: bool
    - name: truncated
      list: bool
    - name: pose
      list: string
  splits:
  - name: train
    num_bytes: 635750358
    num_examples: 5717
  - name: validation
    num_bytes: 635709020
    num_examples: 5823
  download_size: 1341225715
  dataset_size: 1271459378
- config_name: segmentation
  features:
  - name: id
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  splits:
  - name: train
    num_bytes: 166090210
    num_examples: 1464
  - name: validation
    num_bytes: 164358042
    num_examples: 1449
  download_size: 332295988
  dataset_size: 330448252
- config_name: segmentation_aug
  features:
  - name: id
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  splits:
  - name: train
    num_bytes: 1112130120
    num_examples: 10582
  - name: validation
    num_bytes: 164358042
    num_examples: 1449
  download_size: 1407690427
  dataset_size: 1276488162
configs:
- config_name: detection
  data_files:
  - split: train
    path: detection/train-*
  - split: validation
    path: detection/validation-*
- config_name: segmentation
  data_files:
  - split: train
    path: segmentation/train-*
  - split: validation
    path: segmentation/validation-*
  default: true
- config_name: segmentation_aug
  data_files:
  - split: train
    path: segmentation_aug/train-*
  - split: validation
    path: segmentation_aug/validation-*
---

# PASCAL VOC 2012

PASCAL VOC 2012, packed once with a config per task view, so that a project can pull the split it
needs without unpacking `VOCtrainval_11-May-2012.tar` and wiring up paths first.

```python
from datasets import load_dataset

seg = load_dataset("shijli/voc2012", "segmentation")            # 1464 train / 1449 val
aug = load_dataset("shijli/voc2012", "segmentation_aug")        # 10582 train / 1449 val
det = load_dataset("shijli/voc2012", "detection")               # 5717 train / 5823 val
```

## Configs

| config | train | val | columns |
|---|---|---|---|
| `segmentation` (default) | 1464 | 1449 | `id`, `image`, `mask` |
| `segmentation_aug` | 10582 | 1449 | `id`, `image`, `mask` |
| `detection` | 5717 | 5823 | `id`, `image`, `width`, `height`, `objects` |

`id` is the original VOC image id (`2007_000032`), so any row joins back to a local VOC checkout.

VOC 2012 withholds its test annotations, so there is no test split.

## Masks are labels, not pictures

**A mask pixel's value is the class id.** Masks are palette (mode `P`) PNGs, exactly as VOC ships
them: 0 is background, 1–20 are the classes below, and **255 marks the void border**, which is
excluded from both training and scoring rather than being a 21st class.

```python
import numpy as np
mask = np.array(seg["val"][0]["mask"])   # uint8, values in {0..20, 255}
```

Do **not** call `.convert("RGB")` on a mask: that turns class ids into display colors and destroys
the labels. The palette attached to each mask is for viewing only. Several VOC mirrors store masks
as RGB, which forces every consumer to reverse the VOC colormap before the labels mean anything;
this one does not.

## Classes

`background` (0), then `aeroplane`, `bicycle`, `bird`, `boat`, `bottle`, `bus`, `car`, `cat`,
`chair`, `cow`, `diningtable`, `dog`, `horse`, `motorbike`, `person`, `pottedplant`, `sheep`,
`sofa`, `train`, `tvmonitor` (1–20). The `detection` config's `objects.label` is a `ClassLabel`
over the 20 object classes only, with no background entry, so its indices run 0–19.

## The augmented split

`segmentation_aug` is the standard 10582-image `train_aug` set: the SBD annotations minus every
image held out for validation. Its **train masks come from SBD, not from VOC**. Its val split uses
the official VOC masks, the same 1449 images `segmentation` scores on, so a model trained on the
augmented set is measured against the same ground truth as one that was not.

## Detection boxes

`objects` is a struct of parallel lists: `label`, `bbox`, `difficult`, `truncated`, `pose`.

```python
r = det["train"][0]
for label, box in zip(r["objects"]["label"], r["objects"]["bbox"]):
    ...
```

Boxes are `[xmin, ymin, xmax, ymax]` exactly as the VOC XML gives them, which is **1-indexed and
inclusive on both corners**. They are not converted, so they compare directly against the original
annotation files. The `difficult` flag is carried through rather than filtered, since whether to
drop those objects is the evaluation protocol's decision.

## How this was packed

`create_dataset.py` in this repo is the script that produced these parquet files, from an unpacked
VOC2012 folder. Masks that already carry the VOC palette are published byte for byte; the SBD
masks, which ship as mode `L` with the same pixel values, are re-tagged as mode `P` with the VOC
palette so that every config decodes the same way.

Every mask in every split was checked against the source files after packing: all 14944 of them
decode to arrays identical to the originals, with the void marker intact.

The same image appears in more than one config, since each config is self-contained. That is
deliberate: it means loading `segmentation` downloads that split alone rather than the whole
image pool.

## License and attribution

The images come from flickr and remain under the copyright of their respective owners. They are
distributed for non-commercial research and educational use under the terms of the
[PASCAL VOC challenge](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/). This packaging adds no
rights: use it under those same terms and cite the original work.

```bibtex
@article{everingham2015pascal,
    title={The Pascal Visual Object Classes Challenge: A Retrospective},
    author={Everingham, Mark and Eslami, S. M. Ali and Van Gool, Luc and
            Williams, Christopher K. I. and Winn, John and Zisserman, Andrew},
    journal={International Journal of Computer Vision},
    volume={111}, number={1}, pages={98--136}, year={2015}
}
```

The `segmentation_aug` train masks are from SBD and should be cited separately:

```bibtex
@inproceedings{hariharan2011semantic,
    title={Semantic Contours from Inverse Detectors},
    author={Hariharan, Bharath and Arbelaez, Pablo and Bourdev, Lubomir and
            Maji, Subhransu and Malik, Jitendra},
    booktitle={International Conference on Computer Vision (ICCV)},
    year={2011}
}
```