| --- |
| 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} |
| } |
| ``` |
|
|
