license: cc-by-nc-4.0
task_categories:
- visual-question-answering
- image-classification
- object-detection
language:
- en
tags:
- biology
- biodiversity
- taxonomy
- tree-of-life
- fine-grained
- spatial-reasoning
- counting
- re-identification
- animal-re-identification
- benchmark
- vision-language-models
- imageomics
pretty_name: Bio-VQA
size_categories:
- 1M<n<10M
configs:
- config_name: lite
default: true
data_files:
- split: C1
path: lite/C1.csv
- split: C2
path: lite/C2.csv
- split: C3
path: lite/C3.csv
- split: C4
path: lite/C4.csv
- config_name: C1_taxonomy
data_files:
- split: amphibians
path: C1/*Amphibians*.csv
- split: angiosperms
path: C1/*Angiosperms*.csv
- split: arachnids
path: C1/*Arachnids*.csv
- split: birds
path: C1/*Birds*.csv
- split: bryophytes
path: C1/*Bryophytes*.csv
- split: fish
path: C1/*Fish*.csv
- split: gymnosperms
path: C1/*Gymnosperms*.csv
- split: insects
path: C1/*Insects*.csv
- split: mammals
path: C1/*Mammals*.csv
- split: reptiles
path: C1/*Reptiles*.csv
- config_name: C2_attributes
data_files:
- split: test
path: C2/*.csv
- config_name: C3_spatial
data_files:
- split: counting
path: C3/C3_Counting.csv
- split: relative_counting
path: C3/C3_Counting-Relative_*.csv
- split: absolute_distance
path: C3/C3_Distance-Absolute.csv
- split: relative_distance
path: C3/C3_Distance-Relative.csv
- split: localization
path: C3/C3_Localization*.csv
- split: relative_position
path: C3/C3_Relative_Position*.csv
- split: viewpoint
path: C3/C3_Viewpoint*.csv
- split: ocr
path: C3/C3_OCR*.csv
- config_name: C4_reidentification
data_files:
- split: easy
path: C4/C4_*_easy.csv
- split: random
path: C4/C4_*_random.csv
- split: hard
path: C4/C4_*_hard.csv
How Finely Can Vision–Language Models See Biology?
Bio-VQA: A Benchmark for Taxonomic, Morphological, Spatial and Individual Reasoning
S M Rayeed · Qian Ma · Charles V. Stewart · Yao Ma
🌐 Project page | 💻 Github Repo | 📄 Paper: WACV 2027 (link will be added here) | ⚡ Bio-VQA-Lite
Dataset summary
Bio-VQA is a biological visual question answering benchmark: 1,861,608 questions over 216,715 images drawn from 11 public datasets, organized into four components and 24 tasks. Together they cover four aspects of biological visual understanding that prior benchmarks treat separately, or not at all:
| Component | What it asks | Tasks | Images | Questions | Source datasets | |
|---|---|---|---|---|---|---|
| C1 | Hierarchical taxonomic reasoning | Which taxon, at eight ranks? | 9 | 100,000 | 890,000 | TreeOfLife-200M |
| C2 | Fine-grained attribute grounding | Which trait, on which part, on which side? | 4 | 23,576 | 824,104 | CUB-200-2011 |
| C3 | Quantitative spatial reasoning | How many, how far, where, facing where? | 8 | 78,139 | 132,504 | WAID, Boxes on Bees, 2018-NEON-Beetles, DePondFi, GZCD, WhaleSharkID, HyenaID, LeopardID |
| C4 | Individual re-identification | Is this the same animal? | 3 | 15,000 | 15,000 | WhaleSharkID, BelugaID, HyenaID, LeopardID, GZCD |
| Total | 24 | 216,715 | 1,861,608 | 11 (four serve both C3 and C4) |
Three design rules run through the whole benchmark:
- Ground truth comes only from existing annotations. A question is generated only where a source annotation (lineage, attribute, box, count, measurement, viewpoint, individual ID) supplies the answer. No answer key depends on our own labeling, so every answer can be checked against its source.
- Every task has a reference point. Each task carries an image-blind floor (the best a rule that never looks at the image can score), and a domain specialist ceiling where one exists (BioCLIP-2 for C1, MegaDescriptor for C4).
- Graded scoring. A near miss scores higher than a distant miss: Wu–Palmer and Lin similarity for C1, CAGS for C2, CAS and lift over the floor for C3, d′ and criterion c for C4. Each graded score is reported next to the exact match it modifies.
This card describes the data. The question generators, the 18 model runners and the scorers are in the GitHub repository. Figures and examples are on the project page.
Contents
- Quick start
- Repository layout
- Configurations and splits
- Components in detail
- Bio-VQA-Lite
- Data fields
- Images
- Model predictions
- How the data were built
- Scoring
- Results summary
- Intended use and limitations
- Licenses
- Citation
Quick start
from datasets import load_dataset
lite = load_dataset("imageomics/Bio-VQA", "lite") # splits: C1, C2, C3, C4 (12,000 questions)
c3 = load_dataset("imageomics/Bio-VQA", "C3_spatial", split="counting")
c4 = load_dataset("imageomics/Bio-VQA", "C4_reidentification", split="hard")
Images for C2 marker localization, C3 and C4 are stored in zip shards next to the question files. Each row names its
shard (image_zip) and the file inside it (image_in_zip):
import io, zipfile
from huggingface_hub import hf_hub_download
from PIL import Image
def load_image(component, image_zip, image_in_zip):
"""component: 'C2', 'C3' or 'C4'"""
path = hf_hub_download("imageomics/Bio-VQA", f"{component}/{image_zip}", repo_type="dataset")
with zipfile.ZipFile(path) as z:
return Image.open(io.BytesIO(z.read(image_in_zip))).convert("RGB")
row = c4[0]
img = load_image("C4", row["image_zip"], row["image_in_zip"])
To download a whole component at once (for example C3):
from huggingface_hub import snapshot_download
snapshot_download("imageomics/Bio-VQA", repo_type="dataset", allow_patterns=["C3/*"], local_dir="Bio-VQA")
C1 images (TreeOfLife-200M) and the original C2 photographs (CUB-200-2011) are not re-hosted here. See Images for how to get them.
Scoring a model: add a column named <model>_answer with your model's raw replies and run the matching scorer
from the GitHub repository. The scorers do all parsing and
normalization, the same way for every model.
Repository layout
lite/ Bio-VQA-Lite: C1.csv, C2.csv, C3.csv, C4.csv — 500 questions per task, 12,000 in total
C1/ question files, one per organismal group (10 files); images referenced by TreeOfLife-200M uuid
C2/ question file(s); marker-localization renders as zip shards
C3/ one question file and its image shards per task: C3_<Task>.csv + C3_<Task>_NNN.zip
C4/ one question file and its image shards per species × condition:
C4_<Species>_<easy|random|hard>.csv + C4_<Species>_<condition>_NNN.zip
predictions/ per-item answers of all 18 models and both specialists, exactly as scored in the paper
Shards hold at most 10,000 images each. The large Grevy's zebra pair images use 300 per shard to keep files small.
Configurations and splits
| Config | Splits | Rows | Notes |
|---|---|---|---|
lite (default) |
C1, C2, C3, C4 |
12,000 | 500 per task; the task column names the task |
C1_taxonomy |
one per organismal group: amphibians … reptiles |
890,000 | 9 question types per image (8 for reptiles) |
C2_attributes |
test |
824,104 | 10 question families in 4 categories |
C3_spatial |
counting, relative_counting, absolute_distance, relative_distance, localization, relative_position, viewpoint, ocr |
132,504 | one split per task |
C4_reidentification |
easy, random, hard |
15,000 | 5 species × 1,000 pairs per split |
Bio-VQA is an evaluation benchmark. All splits are test data, and there is no training split.
Components in detail
C1 — Hierarchical taxonomic reasoning
How far does a generalist VLM stand from a taxonomic specialist, and at which rank does it break down?
- Source: a 100K-image subset of TreeOfLife-200M, 10,000 images from each of 10 organismal groups. Six of those groups do not appear in prior biological VQA benchmarks.
- Coverage: 626 families, 2,311 genera, 3,403 species, which is 6× VLM4Bio's genus coverage and 4× its species coverage.
- Sampling constraints: (a) a complete lineage from phylum to species, plus a common name, so no question lacks a ground truth; (b) a minimum image resolution; (c) a per-species cap, so abundant species do not take over a group.
| Group | GBIF Backbone filter |
|---|---|
| Amphibians | class Amphibia |
| Angiosperms | class Magnoliopsida, Liliopsida |
| Arachnids | class Arachnida |
| Birds | class Aves |
| Bryophytes | phylum Bryophyta |
| Fish | class Actinopterygii, Teleostei, Elasmobranchii, Holocephali, Coelacanthi, Dipneusti |
| Gymnosperms | class Pinopsida |
| Insects | class Insecta |
| Mammals | class Mammalia |
| Reptiles | class Squamata, Testudines, Crocodylia, Sphenodontia |
| Question type | Format | Example answer |
|---|---|---|
| phylum | open | Chordata |
| class | open | Amphibia |
| order | open | Anura |
| family | open | Hylidae |
| genus | open | Dryophytes |
| species epithet | open | cinereus |
| scientific name | open | Dryophytes cinereus |
| common name | open | green treefrog |
| common name | 4-way MCQ | one of four candidates |
- Nine groups have 9 questions per image. Reptiles have 8, because the GBIF Backbone moved Squamata to class rank and the order rank is empty. Total: 890,000.
- Recognition vs recall. The common name is asked twice for the same image and target: once open-ended and once as 4-way multiple choice. The three distractors are drawn uniformly from taxa in the same class as the target.
- Common names in English. GBIF vernacular records include Swedish, Danish and Norwegian names. Every non-English common name is resolved to English against iNaturalist and Wikidata.
- Synonyms. A synonym map built from the GBIF Backbone accepts any synonym of the target. For example, a cougar answered as Felis concolor matches Puma concolor.
- Difficulty tiers. At each rank, taxa are split into thirds by image count in TreeOfLife-200M: the top third is Easy, the middle third Medium and the bottom third Hard.
C2 — Fine-grained attribute grounding
Can a VLM find a trait on the animal, and which side of the animal it is on?
- Source: CUB-200-2011, with 11,788 images and 312 binary
attributes, each a part · property · value triple (
has_wing_color::brown). - Inversion. Asking 312 sparse yes/no questions would be uninformative, because a constant no scores well. So each part and property are merged into one question (What is the wing color?), and the marked value is the answer. Annotator certainty is used to select and filter candidate traits.
- Distractors are other values attested for the same part and property. Any value that is also true of the image is excluded, so exactly one option is correct and process of elimination does not work.
- Marker localization. A marker is rendered on each image, and the question asks which body part it marks. One distractor shares the marker's color but not its shape, another shares its shape but not its color. The 11,788 renders bring the image count to 23,576.
| Category | Family | Questions | Format | Example |
|---|---|---|---|---|
| Morphological attributes | attr_bare_value |
280,433 | MCQ | What is the bill color of this bird? → black |
| Relative salience | attr_group_value |
321,073 | MCQ | Which … is the most prominently visible physical trait …? → breast color: red |
| Anatomical localization | marker_localization |
11,788 | MCQ | What is the body part … marked with the yellow dot? → beak |
visibility_partset |
117,880 | MCQ | Which of the following parts … are visible? → beak | |
visibility_side |
35,364 | MCQ | Which of the eyes of the bird are visible? → Both | |
| Naming | category_mcq |
11,191 | MCQ | What type of bird is this? → Gull |
open_common_name |
11,788 | open | → California Gull | |
open_scientific_name |
11,668 | open | → Larus californicus | |
open_genus |
11,728 | open | → Larus | |
open_kind_of |
11,191 | open | What kind of gull is this? → California | |
| Total | 824,104 | ~94% MCQ |
Answer spaces differ: 15 values for color, 3 for bill length, 4 for visibility_side. CAGS corrects for this per item.
C3 — Quantitative spatial reasoning
Can a VLM count, measure, locate and orient biological subjects?
Following AVA-Bench, spatial reasoning is split into seven atomic tasks plus OCR as a negative control. The source datasets are aggregated into ten 500-image subsets. Count stratification balances sparse and dense scenes, and images whose annotation completeness cannot be verified are dropped. Each task runs only where its ground truth exists.
| Task | Items | Runs on | Answer form | Example |
|---|---|---|---|---|
| Counting | 53,498 | all ten subsets | integer | How many beetles are there in this image? → 11 |
| Relative counting | 42,412 | all ten subsets | one of four panels | Which of the 4 images contains the most number of beetles? → Image B |
| Absolute distance | 14,108 | 2018-NEON-Beetles | number, cm | What is the measurement of the elytra width … with respect to the scalebar …, in cm? → 0.497 |
| Relative distance | 3,976 | 2018-NEON-Beetles | one of four specimens | Which beetle has the largest elytral length? → Beetle D |
| Localization | 3,988 | wildlife catalogs | box, four integers | Provide the bounding box coordinate of the zebra in this image. → [854, 819, 3009, 2322] |
| Relative position | 1,000 | 2018-NEON-Beetles | direction | Where is Beetle (blue) located with respect to Beetle (red)? → Top-right |
| Viewpoint | 13,000 | wildlife catalogs | one of ten orientations | What is the viewpoint of the hyena? → left |
| OCR (control) | 522 | 2018-NEON-Beetles | string | What is the barcode text? → A00000012435 |
| Total | 132,504 | 78,139 images |
- Counting tiers. Every canvas has the same pixel budget. Easy is one image filling the canvas. Medium is two panels from different datasets. Hard is two panels from the same dataset.
- Why beetles. Absolute and relative distance need the recorded trait and scalebar measurements, which only 2018-NEON-Beetles has. Relative position needs specimens lying flat at one focal depth, so that top-right is a real two-dimensional relation and not a perspective artifact.
- Why wildlife catalogs. Localization and viewpoint need a single dominant subject with a recorded box and viewing angle. These come from the C4 catalogs (WhaleSharkID, HyenaID, LeopardID, GZCD).
- Localization is evaluated on the Easy tier.
C4 — Individual re-identification
Does a VLM decide by identity (stripes, spots, scars) or by overall resemblance?
| Catalog | Species | Identity signal |
|---|---|---|
| WhaleSharkID | whale shark | spot pattern |
| BelugaID | beluga whale | scarring, dorsal ridge (changes between sightings) |
| HyenaID | spotted hyena | spot pattern |
| LeopardID | leopard | rosette pattern |
| GZCD | Grevy's zebra | stripe pattern |
Two photographs are placed side by side on one canvas, with one question:
This image shows two images placed side by side. Is the main subject in the left image the very same individual animal as the main subject in the right image?
Difficulty is set with DINOv2 embeddings, retrieved within viewpoint groups:
| Condition | Same-individual pair | Different-individual pair | Appearance vs identity |
|---|---|---|---|
| easy | an animal and its nearest match | two animals that look nothing alike | agree |
| random | drawn without appearance | drawn without appearance | no signal |
| hard (adversarial) | an animal and its furthest sighting | two nearest neighbors | disagree |
Each catalog gives 1,000 pairs per condition, 500 Yes and 500 No, for 15,000 pairs in total. Same-individual
pairs are drawn first because repeat sightings are scarce. In the hard condition, the two animals share pose, lighting,
background and coat tone, and differ only in the markings that identify them. Pair images are stored under neutral
names (img_000001.png, …), because the original file names encode the pair.
Bio-VQA-Lite
A stratified, seeded sample of 500 questions per task, for cheap evaluation and for development.
| Component | Tasks | Stratified by | Rows |
|---|---|---|---|
| C1 | 9 question types | organismal group | 4,500 |
| C2 | 4 categories | question family | 2,000 |
| C3 | 8 tasks | source dataset × tier × subset | 4,000 |
| C4 | 3 conditions | species × answer | 1,500 |
| Total | 24 | 12,000 |
Inside each stratum, sampling is uniform with seed 0, and model-answer columns are dropped. Lite rows keep the image
reference columns of the full release, so the same load_image helper works. The script is
Release/make_lite.py.
Use Lite for development and quick comparisons. Use the full components for numbers you report.
Data fields
The columns differ a little between components. These are the ones you need:
| Column | Where | Meaning |
|---|---|---|
question |
C2, C3, Lite | Question text, exactly as shown to the models |
answer |
C2, C3, Lite | Gold answer |
qa_pairs / qa_pair |
C1 / C4 | JSON {"Q": question, "A": gold answer} |
task |
Lite | Task name within the component |
uuid |
C1 | TreeOfLife-200M image identifier |
source_url |
C1 | URL the image was downloaded from |
taxonomy columns (kingdom … species, common name) |
C1 | Full lineage of the image |
category |
C2 | One of the four C2 categories |
filename |
C2 | Path of the original photograph inside the CUB-200-2011 release |
source_dataset, tier |
C3 | Source subset and difficulty / canvas tier, where the task has one |
image_zip |
C2 (marker rows), C3, C4, Lite | Name of the zip shard that holds the image |
image_in_zip |
C2 (marker rows), C3, C4, Lite | File name inside the shard |
species |
Lite C4 | Catalog of the pair |
import json
q = json.loads(row["qa_pairs"]) # C1; C4 uses row["qa_pair"]
question, gold = q["Q"], q["A"]
A model's reply should be stored verbatim in a <model>_answer column. The scorers parse it.
Images
| Component | Images | Where they are |
|---|---|---|
| C1 | 100,000 | Not re-hosted. Each row has the TreeOfLife-200M uuid and source_url. Fetch them with Release/fetch_c1_images.py, or match on uuid against TreeOfLife-200M. |
| C2 originals | 11,788 | Not re-hosted. Download CUB-200-2011 from the official Caltech release. filename is relative to its images/ root. |
| C2 marker renders | 11,788 | Included (C2/*.zip). These are derived images with a drawn marker, and the answer is defined on them. |
| C3 | 78,139 | Included (C3/C3_<Task>_NNN.zip). Counting canvases and distance overlays are composed by us, and the answer is defined on the composed image. |
| C4 | 15,000 | Included (C4/C4_<Species>_<condition>_NNN.zip). Side-by-side pairs, stored under neutral names. |
Why the split. Images are included wherever the answer is defined on an image we produced: a rendered marker, a composed canvas, a line overlay, a side-by-side pair. These are redistributed under their upstream terms, with per-source attribution. Where a question asks about an unmodified source photograph, we ship the identifier instead.
C1 reproducibility. The C1 download ran concurrently under a per-group budget, so a fresh run of the download
script would select a different 10,000 images per group. The released uuid list is the artifact of record.
Model predictions
predictions/ holds the per-item raw answers of all 18 models, and of BioCLIP-2 (C1; tol, kingdom and group
label sets) and MegaDescriptor (C4, Grevy's zebra), exactly as scored in the paper. Point the scorers in the
GitHub repository at these files to regenerate every table in the paper and
supplement without running any model.
Models: Qwen2.5-VL-3B/7B, Qwen3-VL-8B/30B-A3B/32B, InternVL3-8B/38B, InternVL3.5-8B/30B-A3B/38B, Gemma-3-12B, Gemma-4-12B, Molmo2-8B, Pixtral-12B, Phi-3.5-Vision, Llama-3.2-11B-Vision, LLaVA-NeXT-Vicuna-7B and LLaVA-NeXT-Mistral-7B. All were run zero-shot with greedy decoding and one attempt per item.
How the data were built
| Step | C1 | C2 | C3 | C4 |
|---|---|---|---|---|
| Acquire | DuckDB query over the TreeOfLife-200M catalog; concurrent download; verify complete lineage | CUB-200-2011 | per-dataset download and preprocessing; ten count-stratified 500-image subsets | catalog download |
| Clean | non-English common names → English; GBIF synonym map | annotator-certainty filtering | drop images with unverifiable annotation completeness | viewpoint grouping |
| Generate | 8 ranks + MCQ common name | invert part · property · value; render markers | one generator per task; canvas tiers | DINOv2 pair mining (easy / random / hard) |
| Seeded | ✅ | ✅ | ✅ | ✅ |
The code for every step is in the GitHub repository.
Scoring
| Component | Graded metric | Floor / ceiling |
|---|---|---|
| C1 | exact match, soft exact match (GBIF synonyms), Wu–Palmer, Lin, severity | floor: the most frequent taxon at each rank · ceiling: BioCLIP-2 |
| C2 | CAGS = (E[s] − E₀[s]) / (1 − E₀[s]), with CIEDE2000 distance for colors and co-occurrence for parts | 0 = chance · validated by 3 annotators on 306 items |
| C3 | CAS = max(0, 1 − |n̂ − n| / n) for counting; mean IoU; balanced accuracy; within-20% for distance; lift = (S − φ)/(1 − φ) | image-blind floor φ on every task |
| C4 | accuracy, d′ = z(H) − z(F), criterion c, with log-linear correction | ceiling: MegaDescriptor (Grevy's zebra) |
Results summary
Eighteen open-weight VLMs from 3.75B to 38.4B parameters, evaluated zero-shot (full table and per-model results in the paper and on GitHub):
| Finding | |
|---|---|
| C1 | Exact match falls from .725 at phylum to .123 at scientific name (mean of 18 models). The gap to BioCLIP-2 widens from 1.2× at class (.731 vs .874) to 2× at scientific name (.269 vs .544). Open-ended common names score .039; the same images as 4-way MCQ score .645 (16.4×). |
| C2 | Anatomical localization is the hardest category (.370 CAGS). Given the correct part, 17 of 18 models are at or below chance on left vs right. The exception is pointing-supervised Molmo2-8B. |
| C3 | The best model counts animals exactly on 41.7% of images with 2–10 individuals, against 94.9% on everyday objects in the same range (CountBench). 15 of 18 models fall below an image-blind constant on absolute size measurement, and 12 of 18 fall below a mean-gold-box floor on localization. |
| C4 | MegaDescriptor reaches d′ .733 on Grevy's zebra against a VLM mean of .391. Under adversarial pairing, 17 of 18 models fall below chance (negative d′). |
Across components, a strong model is strong everywhere: InternVL3-38B, Qwen3-VL-30B and Qwen3-VL-32B are top five in every component. Below them, the median model moves 5.5 of 18 places between components.
Intended use and limitations
Intended use. Zero-shot evaluation of vision–language models on biological images, and diagnosis of which ability fails: rank-level taxonomy, trait grounding, laterality, counting, measurement, localization, orientation, or individual identity.
Not intended for training models that are then evaluated on Bio-VQA. It is also not intended for any operational decision about a real animal or population without expert review.
Known limitations
- Images come from public datasets that models may have seen in pretraining. C1 shares its source with BioCLIP-2's training corpus, so BioCLIP-2 is an optimistic upper bound and not a neutral baseline.
- There is no human performance reference. Human raters validated C2 scoring only, and C1, C3 and C4 ground truth was not audited by humans beyond the source annotations.
- Floors are rule-based; a text-only VLM might set a higher one.
- Coverage is uneven by design: C2 is birds only, distance and relative position are beetles only, and C4 has five species.
- C1 common names are English only. C1 inherits the GBIF Backbone's classification, including its treatment of reptiles and fish.
Licenses
Benchmark annotations (questions, answers, Lite, predictions) are released under CC BY-NC 4.0.
Images keep the license of their source dataset:
| Source | Used in | Terms |
|---|---|---|
| TreeOfLife-200M | C1 (not re-hosted) | per-image licenses recorded in TreeOfLife-200M |
| CUB-200-2011 | C2 (renders included, originals not re-hosted) | non-commercial research and educational use |
| WAID | C3 | per the WAID release |
| Boxes on Bees and Pollen | C3 | per the source release |
| 2018-NEON-Beetles | C3 | per the source release |
| DePondFi | C3 | per the source release |
| GZCD (Grevy's zebra) | C3, C4 | per the source release |
| WhaleSharkID, HyenaID, LeopardID | C3, C4 | per the source releases |
| BelugaID | C4 | per the source release |
Check the upstream terms before redistributing any image. The code in the GitHub repository is MIT-licensed.
Acknowledgments.
This research was supported by the Imageomics Institute, funded by the US National Science Foundation's (NSF) Harnessing the Data Revolution (HDR) program under Award No. 2118240. Qian Ma and Yao Ma are supported by NSF under grant number NSF2406648.
Citation
@inproceedings{rayeed2027biovqa,
title = {How Finely Can Vision--Language Models See Biology? {Bio-VQA}: A Benchmark for Taxonomic, Morphological, Spatial and Individual Reasoning},
author = {Rayeed, S M and Ma, Qian and Stewart, Charles V. and Ma, Yao},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year = {2027}
}
Please also cite the source datasets you use. Their references are listed in the paper and in the GitHub README.
Links: 🌐 Project page · 💻 GitHub · 🤗 This dataset
Contact: open an issue on GitHub or a discussion on this page.