--- annotations_creators: - expert-annotated language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - 10K [!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)