---
license: other
license_name: sa-1b-dataset-research-license
license_link: https://huggingface.co/datasets/maelic/Haystack-coco-format/blob/main/LICENSE_SA-1B
task_categories:
  - object-detection
tags:
  - scene-graph-generation
  - panoptic-scene-graph
  - negative-annotations
  - haystack
  - sa-1b
  - coco-format
language:
  - en
pretty_name: Haystack (COCO format)
size_categories:
  - 10K<n<100K
---

# Haystack (COCO format)

A mirror of **Haystack** (Lorenz, Barthel, Kienzle and Lienhart, *Haystack: A Panoptic Scene Graph
Dataset to Evaluate Rare Predicate Classes*, ICCV 2023 workshops), reformatted in the
same COCO-style schema as the other `maelic/*-coco-format` datasets. Haystack's distinguishing
feature is **explicit negative relation annotations**: for an annotated subject–object pair, an
annotator also confirmed predicates that do *not* hold, so evaluation does not have to treat
missing labels as negatives.

It is used by axis A2 (precision against adjudicated negatives) of OV-SGG-Bench.

## Why this mirror exists

The original download location (`myweb.rz.uni-augsburg.de/~lorenjul/haystack/v1/`) returns HTTP 404
as of November 2026. This repository preserves the v1 release byte-for-byte where possible so that
results on Haystack remain reproducible. The authors' code is at
<https://github.com/lorjul/haystack>.

## Licenses

* **Images** are a subset of Meta's SA-1B dataset and are provided under the **SA-1B Dataset
  Research License** (`LICENSE_SA-1B`, with Meta's notice in `NOTICE_SA-1B.md`): research,
  non-commercial use only. By downloading the images you agree to that license. See
  <https://ai.meta.com/datasets/segment-anything/>.
* **Annotations** (relations, negative relations, panoptic masks) are the Haystack authors' work.
  Their release did not state a license. Cite their paper when you use them.

## Samples

Four images drawn at random (fixed seed) from the 61 that have at least two positive relations, two to eight negatives and at most five objects in relations, with the boxes of every object that takes part in an annotated relation. Green lines are positive relations; red lines are negatives an annotator explicitly rejected for the same pair.

<p>
<img src="https://huggingface.co/datasets/maelic/Haystack-coco-format/resolve/main/assets/sample_1.jpg" width="49%">
<img src="https://huggingface.co/datasets/maelic/Haystack-coco-format/resolve/main/assets/sample_2.jpg" width="49%">
<img src="https://huggingface.co/datasets/maelic/Haystack-coco-format/resolve/main/assets/sample_3.jpg" width="49%">
<img src="https://huggingface.co/datasets/maelic/Haystack-coco-format/resolve/main/assets/sample_4.jpg" width="49%">
</p>

## Contents

One split, `test`, with 11,368 images, 2,870 positive relations and 23,174 negative relations over
133 categories (80 things, 53 stuff) and the 56 predicates of PSG. `categories.json` holds
`thing_classes`, `stuff_classes`, `categories` (things then stuff, the index used by `category_id`)
and `predicates` (the index used by `predicate_id`).

| Field | Description |
|---|---|
| `image` | the SA-1B JPEG, original bytes |
| `image_id`, `width`, `height`, `file_name` | as in the source (`sa_XXXXXX/sa_N.jpg`) |
| `pan_seg`, `pan_seg_file_name` | the panoptic segmentation PNG, original bytes |
| `objects` | `id` (index in the image), `bbox` (COCO `[x, y, w, h]`, pixels), `category_id`, `area` (segment area), `iscrowd`, `isthing`, `segment_id` (the segment's id in `pan_seg`) |
| `relations` | positive relations: `id`, `subject_id`, `object_id` (indices into `objects`), `predicate_id` |
| `neg_relations` | adjudicated negative relations, same fields |

## Changes from the source

All mechanical: boxes converted from Detectron2 `XYXY_ABS` to COCO `[x, y, w, h]`; object and
relation ids made image-local indices; images and masks stored with their original bytes, not
re-encoded. A random sample of 50 images was checked byte-for-byte against the source archives.
Source archive SHA-256: `sa1b-subset.zip`
`cff441c94ba7ce3af5b000a5b1648e9610e3357241338698e3bd79cb396bb919`, `v1.zip`
`07401be66aa4f048a3d52e12b624f393166654b42a4275cb5a051de7fe7ebee4`.

## Citation

```bibtex
@inproceedings{lorenz2023haystack,
  title     = {Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes},
  author    = {Lorenz, Julian and Barthel, Florian and Kienzle, Daniel and Lienhart, Rainer},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
  pages     = {62--70},
  year      = {2023},
  doi       = {10.1109/ICCVW60793.2023.00013}
}
```