---
title: ODOR Dataset Benchmark
url: https://www.emergentmind.com/topics/odor-dataset
type: topic
---

# ODOR Dataset Benchmark

Searching arXiv for the ODOR dataset and related papers to ground the article in the latest literature.
The **ODOR dataset**—short for **Object Detection for Olfactory References**—is a computer-vision benchmark for fine-grained, instance-level detection of objects in artworks that carry olfactory meaning, including flowers, fruits, vessels, smoke, and related motifs. In its broader release, it contains **4,712** artwork images, **38,116** object-level bounding-box annotations, and **139** fine-grained categories, and is explicitly designed to expose challenges that are atypical in photographic detection benchmarks: artistic abstraction, dense and overlapping instances, peripheral placement, small objects, and a long-tailed category distribution [2507.08384].

## 1. Historical emergence and benchmark lineage

ODOR emerged within the **Odeuropa** research line, which frames olfactory references in European visual heritage as a detection problem. An earlier benchmark was released for the **ICPR2022 ODeuropa Challenge on Olfactory Object Recognition**, where the public portion comprised **2,647** paintings, prints, and drawings with **20,120** bounding boxes, and a private test set contained **1,140** artworks with **15,480** annotations [2301.09878]. That challenge version used **87** leaf-node classes under **13** supercategories and covered historical material dating roughly from **c. 1500–1900**.

The later ODOR release broadens the scope to **139** fine-grained categories organized into **eight** supercategories and emphasizes statistical analysis of the dataset as a detection benchmark in its own right. The motivation is not only olfactory heritage, but also the need for models that remain robust under artistic style variation, subtle fine-grained distinctions, and off-center object placement in artworks [2507.08384].

This continuity is methodologically important. The challenge release established the task setting, COCO-style evaluation, and long-tail difficulty; the later release systematized these issues through a larger annotation set and more extensive baseline and secondary studies. A plausible implication is that ODOR should be read less as a single static corpus than as a benchmark family centered on olfactory object recognition in historical art.

## 2. Data model, taxonomy, and annotation procedure

At the dataset level, ODOR provides **high-resolution artwork images** together with **object-level bounding boxes** in **standard COCO JSON**. Each annotation includes at least **`bbox`**, **`category_id`**, and **`supercategory`**. The final taxonomy was obtained from an original candidate pool of **227** classes, which was pruned to **139** by requiring **at least 16 instances in at least 3 images** for each retained category [2507.08384].

The category system is explicitly fine-grained. Reported supercategory examples include:

| Supercategory example | Subclasses | Instances |
|---|---:|---:|
| flower | 30 | 10,866 |
| fruit | 17 | 7,818 |
| mammal | 14 | 4,972 |
| smoking equipment | 8 | 2,595 |
| other | 36 rare subclasses | 6,356 |

The remaining supercategories named in the release are **vegetable**, **drinking vessel**, and **smoke-related**. The annotation target is not generic object presence, but olfactory relevance in artworks, which explains the prominence of florals, fruits, smoking implements, and vessels.

Annotation quality control combines crowdsourcing and expert review. Annotations were collected via **Amazon Mechanical Turk** and then **verified and corrected in multiple rounds by art-history experts** to ensure both **species-level** and **historically accurate** labeling [2507.08384]. In the earlier challenge release, annotators used **CVAT**, with Amazon Mechanical Turk specifically mentioned for flower subtypes, and were instructed to draw **“tightly fitted”** boxes around every visible instance, including very small and partially occluded objects [2301.09878].

## 3. Statistical structure and sources of difficulty

ODOR is statistically challenging in several distinct senses. The mean number of categories per image is reported as **$\mu_{(\mathrm{cls/img})}=3.7$**, and the mean number of instances per image as **$\mu_{(\mathrm{b/img})}=8.1$**. The class distribution is **long-tailed**, and some images contain **more than 15 instances** or **more than 8 classes** [2507.08384].

A central difficulty is overlap and occlusion. **Over 50%** of instances overlap with at least one other instance, and many share **10–40%** of their area with neighboring objects. This is paired with a marked small-object regime: a **large fraction** of objects occupy **less than 4%** of the image area, whereas only a few exceed **25%** [2507.08384]. Such geometry stresses localization, NMS behavior, and fine-grained classification under limited pixel support.

Another distinctive property is spatial distribution. Unlike **COCO** or **PASCAL**, which are described as having a strong center bias, ODOR objects are reported as **almost uniformly distributed across the canvas**. The paper attributes this to the inclusion of peripheral or background objects such as small flowers, fruits, and smoke [2507.08384]. This suggests that architectural priors tuned to center-biased natural-image corpora may transfer imperfectly.

The class distribution’s complexity can, as noted in the paper, be expressed via Shannon entropy,
$$
H=-\sum_k p_k \log p_k,
$$
where $p_k$ is the fraction of instances in category $k$; however, the paper does **not** report an explicit value for $H$ [2507.08384]. The absence of a reported entropy does not diminish the practical observation that ODOR combines long-tail imbalance with dense clutter and peripheral placement, which is an unusual conjunction among detection benchmarks.

## 4. Evaluation protocol and baseline detection performance

ODOR adopts standard COCO-style detection metrics. The reported evaluation uses **mean Average Precision** over IoU thresholds **0.50:0.05:0.95**, together with **AP\(_{50}\)**, **AP\(_{75}\)**, and size-stratified **AP\(_s\)**, **AP\(_m\)**, and **AP\(_l\)**. The paper defines
$$
\mathrm{mAP}=\frac{1}{|C|}\sum_{c\in C} AP_c,\qquad
AP_c=\int_0^1 p_c(r)\,dr,
$$
where $p_c(r)$ is precision as a function of recall for class $c$ [2507.08384].

The benchmark spans two-stage, transformer-based, one-stage anchor-free, and hybrid detectors. Reported test results include the following:

| Model | AP | AP\(_{50}\) |
|---|---:|---:|
| F-RCNN (ResNet-50) | 10.1 | 19.3 |
| DINO (ResNet-50) | 13.3 | 22.2 |
| MADet (ResNet-50) | 10.2 | 18.7 |
| FCOS (ResNet-50) | 8.0 | 14.3 |
| YOLO-v8L | 18.5 | 29.0 |
| DINO + FocalNet-L | 22.6 | 36.5 |

Within Faster R-CNN, stronger backbones improve performance up to **AP = 19.0** with **SWIN-L**. The best reported system overall is **DINO + FocalNet-L**, with **AP = 22.6**, **AP\(_{50}\) = 36.5**, and **AP\(_{75}\) = 23.5** [2507.08384].

The secondary analyses clarify why these numbers remain modest. When predictions are evaluated only at the **supercategory** level, AP increases by **35–60%** across methods; for **DINO + FocalNet-L**, **subclass AP = 22.6%** rises to **supercategory AP = 32.5%**. This isolates fine-grained subclass discrimination—not mere localization—as a major bottleneck. Size-based evaluation reaches approximately **AP\(_s\) = 12.2%**, **AP\(_m\) = 24.3%**, and **AP\(_l\) = 40.3%**, confirming that small-object detection is the principal failure mode [2507.08384].

Class-wise results further show that frequency alone does not determine difficulty. **Lizard** with **31** training instances and average size **22,355 px\(^2\)** attains **AP = 67.4%**, whereas **grapes**, despite **1,576** training instances and average size **7,086 px\(^2\)**, achieve only **AP = 13.8%** because of occlusion and background blending. **“Jan Steen Jug”** with **37** instances and average size **40,234 px\(^2\)** reaches **AP = 63.0%** [2507.08384]. The implied lesson is that object scale, visual separability, and contextual crowding can dominate raw sample count.

## 5. Research uses, methodological lessons, and recommended practices

ODOR is positioned at the intersection of **digital humanities**, **cultural heritage computing**, and **computer vision**. Concrete use cases listed for the dataset include **quantitative art history**, such as tracing the appearance of smoking vessels or roses over time; **iconographic analysis** of historically significant objects like tobacco pipes or incense burners; and “**material turn**” studies concerned with the social life of things, sensory history, and microhistory [2507.08384].

For machine learning, ODOR is described as a **mid-sized, richly annotated** resource that can support **multi-stage fine-tuning** from natural-image datasets toward artistic domains. The available metadata—**artist**, **year**, **Iconclass codes**, and **image titles/descriptions**—can be exploited for **multimodal** or **multi-task** learning, including **CLIP fine-tuning** and **image-text co-training** [2507.08384]. This suggests that ODOR is not merely a detector benchmark but also a substrate for representation learning on visually and historically structured cultural material.

The earlier challenge documentation identifies several methodological directions that are especially compatible with ODOR’s failure modes: **domain adaptation** from photo-trained backbones, **hierarchical detection** that can back off to supercategory labels when fine-grained confidence is low, and **few-shot learning** or **re-balancing losses** such as **focal loss** and **class-balanced sampling** for tail classes [2301.09878]. It also reports that the dominant recipe in top-ranked challenge systems combined **pre-training on ImageNet or COCO** with fine-tuning at moderate learning rates, while **mild geometric and color augmentations** were sufficient and more exotic augmentations did not consistently help.

Future directions named for the later dataset include **semi-automated annotation** to enlarge the current image base, inclusion of **additional textual metadata**, more robust URL hosting, and future ODOR challenges that would push toward **instance segmentation** and even rarer classes [2507.08384].

## 6. Terminological ambiguity: ODOR in other olfactory machine-learning literatures

The label **“ODOR dataset”** is not unique to artwork object detection, and disambiguation is often necessary in arXiv-era literature. In cheminformatics, **“ODOR”** has also been used for an **ordinal odor strength** dataset containing **2,056 molecules** labeled into **odorless**, **low**, **medium**, and **high** categories, assembled from **Good Scents** and **PubChem** and released for structure-based prediction of odor intensity [2512.08683].

A separate molecular resource associated with odor taxonomies aggregates **6,711 unique molecules** annotated with **146 distinct odor descriptors**, together with an **Expert Taxonomy** and a **Data-driven Taxonomy**; its files include **MMD.csv**, **descriptors_matrix.csv**, **ET_hierarchy.json**, and **DT_partition.csv** [2508.09217].

| ODOR usage | Domain | Scale |
|---|---|---|
| Object Detection for Olfactory References | artwork object detection | 4,712 images; 38,116 annotations; 139 categories |
| Ordinal odor strength dataset | molecular odor-strength prediction | 2,056 molecules; 4 ordinal bins |
| Odor taxonomy dataset | structure-based odor prediction | 6,711 molecules; 146 descriptors |

For scholarship on visual cultural heritage, **ODOR** ordinarily denotes the **Object Detection for Olfactory References** benchmark [2507.08384]. In molecular olfaction, however, the same label can refer to chemically grounded datasets with entirely different units of analysis, target variables, and evaluation protocols [2512.08683][2508.09217]. This naming overlap is a practical source of citation ambiguity and should be resolved explicitly in technical writing.

Source: https://www.emergentmind.com/topics/odor-dataset