| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: image_id |
| dtype: int64 |
| - name: file_name |
| dtype: string |
| - name: width |
| dtype: int32 |
| - name: height |
| dtype: int32 |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float32 |
| length: 4 |
| - name: category |
| list: |
| class_label: |
| names: |
| '0': ant |
| '1': camel |
| '2': jewellery |
| '3': frog |
| '4': physalis |
| '5': celery |
| '6': cauliflower |
| '7': pepper |
| '8': ranunculus |
| '9': chess flower |
| '10': cigarette |
| '11': matthiola |
| '12': cabbage |
| '13': earring |
| '14': dandelion |
| '15': neroli |
| '16': dragonfly |
| '17': hyacinth |
| '18': reptile/amphibia |
| '19': apricot |
| '20': snake |
| '21': lizard |
| '22': asparagus |
| '23': spring onion |
| '24': snowflake |
| '25': moth |
| '26': poppy |
| '27': columbine |
| '28': rabbit |
| '29': geranium |
| '30': crab |
| '31': radish |
| '32': big cat |
| '33': jan steen jug |
| '34': monkey |
| '35': snail |
| '36': bellflower |
| '37': lilac |
| '38': pot |
| '39': peony |
| '40': coffeepot |
| '41': hazelnut |
| '42': censer |
| '43': artichoke |
| '44': dahlia |
| '45': sniffing |
| '46': fly |
| '47': deer |
| '48': caterpillar |
| '49': garlic |
| '50': blackberry |
| '51': chalice |
| '52': lobster |
| '53': necklace |
| '54': bug |
| '55': insect |
| '56': prawn |
| '57': bracelet |
| '58': carrot |
| '59': cornflower |
| '60': pumpkin |
| '61': orange |
| '62': walnut |
| '63': cat |
| '64': daisy |
| '65': forget-me-not |
| '66': carafe |
| '67': match |
| '68': beer stein |
| '69': tobacco-box |
| '70': violet |
| '71': pomander |
| '72': bottle |
| '73': candle |
| '74': heliotrope |
| '75': wine bottle |
| '76': strawberry |
| '77': pomegranate |
| '78': whale |
| '79': lily of the valley |
| '80': iris |
| '81': tobacco |
| '82': olive |
| '83': tobacco-packaging |
| '84': meat |
| '85': daffodil |
| '86': melon |
| '87': fire |
| '88': petunia |
| '89': mushroom |
| '90': teapot |
| '91': ring |
| '92': pig |
| '93': ashtray |
| '94': cheese |
| '95': onion |
| '96': cup |
| '97': nut |
| '98': fig |
| '99': drinking vessel |
| '100': donkey |
| '101': holding the nose |
| '102': lily |
| '103': smoke |
| '104': bread |
| '105': currant |
| '106': glass without stem |
| '107': anemone |
| '108': mammal |
| '109': chimney |
| '110': smoking equipment |
| '111': bivalve |
| '112': butterfly |
| '113': gloves |
| '114': lemon |
| '115': horse |
| '116': plum |
| '117': jasmine |
| '118': pear |
| '119': glass with stem |
| '120': vegetable |
| '121': carnation |
| '122': jug |
| '123': goat |
| '124': fish |
| '125': apple |
| '126': tulip |
| '127': cherry |
| '128': cow |
| '129': animal corpse |
| '130': dog |
| '131': fruit |
| '132': bird |
| '133': rose |
| '134': peach |
| '135': sheep |
| '136': pipe |
| '137': grapes |
| '138': flower |
| - name: area |
| list: float32 |
| - name: iscrowd |
| list: int64 |
| - name: artist |
| dtype: string |
| - name: title |
| dtype: string |
| - name: iconography |
| dtype: string |
| - name: earliest_date |
| dtype: string |
| - name: latest_date |
| dtype: string |
| - name: genre |
| dtype: string |
| - name: material |
| dtype: string |
| - name: photo_archive |
| dtype: string |
| - name: image_credits |
| dtype: string |
| - name: details_url |
| dtype: string |
| - name: iconclass_code |
| dtype: string |
| - name: image_license |
| dtype: string |
| - name: description |
| dtype: string |
| - name: keywords |
| dtype: string |
| - name: language |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 1335652730 |
| num_examples: 4264 |
| - name: test |
| num_bytes: 74738280 |
| num_examples: 448 |
| download_size: 738282481 |
| dataset_size: 1410391010 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: test |
| path: data/test-* |
| license: cc-by-4.0 |
| task_categories: |
| - object-detection |
| tags: |
| - art |
| - artwork |
| - object-detection |
| - digital-humanities |
| - iconclass |
| - glam |
| - cultural-heritage |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # ODOR — Object Detection for Olfactory References in Artworks |
|
|
| 4,712 artwork images with **38,165 bounding-box annotations** across **139 fine-grained |
| categories** of smell-related objects — flowers, fruit, censers, animals, vessels — drawn from |
| European art. |
|
|
| Computer vision on artworks is hard in ways photographic benchmarks are not: artistic |
| abstraction, peripheral objects, and fine-grained distinctions between visually similar |
| classes. ODOR is built to test exactly that. |
|
|
| ## What's in a row |
|
|
| Each row carries the image, its detections, **and its art-historical metadata** — the Zenodo |
| deposit ships these as separate files, and they are joined here: |
|
|
| - `image`, `image_id`, `file_name`, `width`, `height` |
| - `objects` — `bbox` (COCO `xywh`), `category` (`ClassLabel`, 139 names), `area`, `iscrowd` |
| - `artist`, `title`, `iconography`, **`iconclass_code`**, `genre`, `material` |
| - `earliest_date`, `latest_date`, `photo_archive`, `image_credits`, `details_url`, |
| `description`, `keywords`, `language` |
| |
| Where present, `iconclass_code` connects this dataset to |
| [`biglam/brill_iconclass`](https://huggingface.co/datasets/biglam/brill_iconclass) — but note it is |
| sparse (see caveats). |
| |
| ## Splits |
| |
| | Split | Images | |
| |---|---| |
| | train | 4,264 | |
| | test | 448 | |
| |
| ## Caveats |
| |
| - **Metadata coverage is uneven.** `artist` is present on 96.6% of rows, but **`iconclass_code` on only 541 of 4,264 (12.7%)**. Do not assume Iconclass coverage. |
| - Metadata is **multilingual** (`language` is `de` for much of the Städel material) and not normalised across source archives. |
| - `earliest_date` / `latest_date` are inconsistent in the source — some are years, some centuries (e.g. `"18"`). Left as strings rather than guessed at. |
| - Images come from several photo archives with differing terms; `image_credits` and `details_url` point back to the originals. |
|
|
| ## Source & credit |
|
|
| Mathias Zinnen, Prathmesh Madhu, Ronak Kosti et al. *The Object Detection for Olfactory |
| References (ODOR) Dataset* (v3.0.2). Zenodo, 2024-04-26. |
| <https://zenodo.org/records/11070878> — CC-BY-4.0. |
|
|
| Produced in the context of the **Odeuropa** project. This repository joins the deposit's COCO |
| annotations with its `meta.csv` and converts to Parquet. Please cite the original authors. |
|
|
