README.md

---
annotations_creators:
  - expert-annotated
language_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - imagenet
task_categories:
  - object-detection
  - image-classification
task_categories::
  - multi-label-image-classification
  - object-detection
pretty_name: "ReImageNet"
tags:
  - imagenet
  - reannotation
  - multi-label
  - bounding-box
  - computer-vision
  - attributes
  - localization
configs:
- config_name: default
  data_files:
    - split: test
      path: reannotation.jsonl
      
extra_gated_prompt: "This dataset is released for research purposes only. By requesting access, you agree to: (1) cite the associated paper in any publication or project that uses this dataset; (2) not redistribute the dataset or its annotations without permission from the authors."

extra_gated_fields:
  I agree to the terms above: checkbox
---


> [!IMPORTANT]
> **Note for reviewers:** This dataset uses Hugging Face gated access 
> with automatic approval to comply with NeurIPS submission requirements. 
> Unfortunately, Hugging Face automatically shares your username and email 
> with the dataset authors upon access request — we cannot disable this. 
> To preserve anonymity during review, please either create a new 
> anonymous Hugging Face account to request access, or use the copy of 
> the dataset included in the supplementary material of our submission, 
> which requires no authentication.
> 
# ReImageNet

**ReImageNet** is a complete multilabel reannotation with localization of the ImageNet-1K validation 
set (ILSVRC2012). A team of 7 trained in-house annotators reviewed all 
50,000 validation images through an iterative annotation process, producing 
per-image bounding boxes with class labels and annotation attributes, 
correcting and extending the original single-label ground truth. Class 
names and definitions were revised where the original WordNet-based names 
no longer matched the actual image content.

> **Note on images:** This repository contains **annotations only**. 
> The images are part of the ImageNet dataset and must be obtained 
> separately from [image-net.org](https://image-net.org/download).
> Match images to annotations using the `file_path` field 
> (e.g. `n01440764/ILSVRC2012_val_00000293.JPEG`).

---

## Dataset Summary

| Property | Value |
|---|---|
| Base dataset | ImageNet-1K validation set (ILSVRC2012) |
| Images | 50,000 |
| Bounding boxes | 99,534 |
| ImageNet classes | 1,000 |
| Labels per image | mean 1.63, median 1 |
| Bounding boxes per image | mean 1.99, median 1 |
| Single-label images (S) | 62.6% |
| Multi-label images (M) | 32.7% |
| No valid label images (N) | 4.7% |
| Annotators | 7 trained non-domain-experts |
| License (annotations) | CC BY 4.0 |

---

## Dataset Structure

### Files

| File | Description |
|---|---|
| `reannotation.jsonl` | Main annotation file — one JSON record per line |
| `label_names.json` | List of 1,000 ImageNet synset IDs indexed by class integer (0–999) |
| `class_update_config.json` | Configuration file containing equivalent classes (visually 
indistinguishable ImageNet class pairs treated as interchangeable during 
evaluation). |

### Data Fields

Each line in `reannotation.jsonl` is a JSON object with the following fields:

| Field | Type | Description |
|---|---|---|
| `image_name` | `string` | Filename (e.g. `ILSVRC2012_val_00004410.JPEG`) |
| `original_class` | `int[]` | Original ImageNet label(s). Usually a single integer; some images have two labels due to ImageNet equivalent classes |
| `reannotated_labels` | `int[]` | All class labels visible in the image, as determined by annotators |
| `file_path` | `string` | Relative path within the ImageNet val directory (`{synset}/{image_name}`) |
| `bboxes` | `object[]` | List of bounding box annotations (see below) |

All class integers are indices into `label_names.json` (0-indexed).

#### Bounding Box Fields

Each element of `bboxes` is:

| Field | Type | Description |
|---|---|---|
| `coordinates` | `int[4]` | Bounding box in pixel space: `[x1, y1, x2, y2]` (top-left, bottom-right). Boxes enclose the object with a moderate margin. |
| `labels` | `int[]` | Class label(s) for this box (index into `label_names.json`) |
| `group` | `int \| null` | Group ID. When multiple bbox entries share the same non-null `group` value, they represent **the same physical object** with multiple labels. Coordinates are identical across grouped entries. |
| `crowd_flag` | `bool` | True if the bbox covers five or more instances of the same class collapsed into a single box. Exact instance count is not recorded. |
| `reflected_flag` | `bool` | True if the object is a reflection in a mirror or water surface, annotated independently of whether the reflected object itself is also visible. |
| `rendition_flag` | `bool` | True if the object is an artificial or stylized representation (toy, drawing, sculpture, logo, etc.) rather than a real instance. |
| `ocr_needed_flag` | `bool` | True if correct classification requires reading text visible in the image. |
| `dominant_object` | `bool` | True if the object is one a person would notice immediately upon viewing the image. Based on annotator judgment rather than size alone. |


> **Label interpretation note:** An empty `reannotated_labels` list (`[]`) 
> indicates the image contains no valid ImageNet class — corresponding to 
> the `N` category in the paper, where annotators were confident no 
> ImageNet object is present. A label with value `-1` indicates the annotator 
> was uncertain about the correct label (marked as *Not Sure* during 
> annotation); these images are flagged for verification in the ongoing 
> verififcation phase.

### label_names.json

A JSON array of 1,000 WordNet synset IDs ordered by class index:

```json
["n01440764", "n01443537", ..., "n15075141"]
```

`label_names[i]` is the synset for class integer `i`.

---

### class_update_config.json

Contains two entries. `eq_classes` lists pairs of ImageNet classes that 
are visually indistinguishable or semantically equivalent (e.g. 
`laptop`/`notebook computer`, `bathtub`/`tub`), and are therefore treated 
as interchangeable during evaluation — a prediction of either class is 
counted as correct. `metadata` records the creation date and the date of 
the last update of this list.

---

## Evaluation code

Evaluation code is available at 
[github.com/klarajanouskova/ImageNet](https://github.com/klarajanouskova/ImageNet)

## Annotation Attributes

Each bounding box can carry one or more attributes that capture visually 
distinct properties of the depicted object:

- **Rendition** — the object is a toy, drawing, sculpture, or other 
  artificial representation. Tests model robustness to non-real-world 
  depictions.
- **Crowd** — five or more instances of the same class, collapsed into a 
  single bounding box. The exact count is not recorded.
- **Text-recognition** — correct classification requires reading visible 
  text. Tests whether models can leverage text as a recognition cue.
- **Reflection** — the object appears as a reflection in a mirror or 
  water surface, annotated regardless of whether the original object is 
  also visible.
- **Dominant** — the object would be immediately noticed upon viewing the 
  image. An image may have any number of dominant objects, or none at all.


---

## Annotation Process

Annotations were produced by a team of 7 non-domain-expert annotators 
(aged 16–50, spanning diverse backgrounds including geology specialists, 
canine enthusiasts, and automotive buffs) recruited and trained in-house.

The annotation process was iterative:

1. **Training**: Annotators studied known ImageNet issues, worked through 
   attribute examples, and practised on intentionally challenging classes. 
   Only annotators passing a quality threshold proceeded to real tasks.
2. **Per-class preparation**: Before labelling any image, annotators 
   examined the actual image content of each class, consulted external 
   references (Wikipedia, iNaturalist), and recorded a working definition 
   in a shared table.
3. **Image labelling**: Annotators drew bounding boxes around all objects 
   a person would notice at first glance, assigned class labels and 
   attributes, assisted by OWLv2 bounding box proposals and OpenCLIP 
   top-20 class predictions.
4. **Verification round**: Annotators revisited already-annotated images 
   using finalised guidelines, with OWLv2/OpenCLIP replaced by MLLM 
   predictions with SAM3-generated localisations as a stronger reference. 
   This process is ongoing.

Quality was continuously monitored via control sets and a supervised 
communication channel.

---

## Considerations for Using the Data

### Limitations

- All annotators share a European background (Czechia, Ukraine, Greece, 
  Latvia), which may affect interpretation of culturally specific classes.
- Fine-grained wildlife classes were annotated by non-experts using 
  online references; species-level distinctions may be imprecise.
- The shared class definition table enforces consistency but may propagate 
  errors if a class is defined incorrectly.
- Model predictions (anonymised, optional) may have nudged annotators 
  toward certain labels.
- This reannotation covers only the validation set. Due to the 
  distribution shift between training and validation sets, revised class 
  names and definitions may not accurately reflect the training set.
- The verification round is ongoing; some annotations may still be 
  updated.

### Social Impact

This dataset extends ImageNet-1k validation labels to support multilabel 
evaluation and spatial grounding, enabling more accurate measurement of 
model performance. The attribute annotations allow fine-grained analysis 
of model capabilities across distinct recognition regimes (text-based, 
rendition-based, etc.).

---


## Related Work

- Flaws of ImageNet — 
  our prior analysis of ImageNet issues, [arXiv](https://arxiv.org/abs/2412.00076), [ICLR blogpost](https://github.com/klarajanouskova/ImageNet/)
- [Multimodal Large Language Models as Image Classifiers](https://arxiv.org/abs/2603.06578) — 
  partial reannotation and MLLM evaluation study
- [Aiming for Perfect ImageNet-1K](https://klarajanouskova.github.io/ImageNet/) - project page

---

## Dataset Card Contact

[c1rcuslegend](akelloillya@gmail.com)