Datasets:
metadata
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,heightobjects—bbox(COCOxywh),category(ClassLabel, 139 names),area,iscrowdartist,title,iconography,iconclass_code,genre,materialearliest_date,latest_date,photo_archive,image_credits,details_url,description,keywords,language
Where present, iconclass_code connects this dataset to
biglam/brill_iconclass — but note it is
sparse (see caveats).
Splits
| Split | Images |
|---|---|
| train | 4,264 |
| test | 448 |
Caveats
- Metadata coverage is uneven.
artistis present on 96.6% of rows, buticonclass_codeon only 541 of 4,264 (12.7%). Do not assume Iconclass coverage. - Metadata is multilingual (
languageisdefor much of the Städel material) and not normalised across source archives. earliest_date/latest_dateare 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_creditsanddetails_urlpoint 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.