---
license: cc-by-4.0
dataset_info:
  features:
    - name: image_id
      dtype: string
    - name: dataset
      dtype: string
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: program
      dtype: string
    - name: reasoning_trace
      dtype: string
    - name: supporting_features
      dtype: string
    - name: category
      dtype: string
    - name: difficulty
      dtype: int32
    - name: verification_attempts
      dtype: int32
    - name: verified
      dtype: bool
    - name: file_name
      dtype: string
    - name: mask_file_name
      dtype: string
  splits:
    - name: train
      num_examples: 1500
---

# SegVQA — Symbolically-Verified VQA Benchmark (v11 labels, n=1500)

Executable & symbolically-verified medical VQA benchmark generated from real polyp
segmentation masks. **500 verified-sampled items per dataset** across Kvasir-SEG,
CVC-ClinicDB, and ETIS-LaribPolypDB.

## Contents

- `benchmarks/segvqa_benchmark_{kvasir,cvc,etis}.json` — the three 500-item benchmarks
  (1500 total), each item carrying: `image_id`, `question`, `answer`, executable DSL
  `program`, `reasoning_trace`, `supporting_features` (per-lesion geometry/appearance/
  position), `category`, `difficulty` (program depth), and the symbolically-verified
  `verified` label.
- `images/{kvasir,cvc,etis,etis-composites}/...` — endoscopy images referenced by
  `file_name`. ETIS comparison/multi-hop items run on synthetic composites (Poisson
  blending), stored separately under `etis-composites` for the organic-vs-composited
  subset split required by the evaluation protocol.
- `masks/{...}` — the ground-truth segmentation masks referenced by `mask_file_name`.

## Verified counts (v11 labels)

| dataset | items | verified | unverified |
|---|---|---|---|
| kvasir | 500 | 480 | 20 |
| cvc | 500 | 476 | 24 |
| etis | 500 | 469 | 31 |

Labels were regenerated with the audited symbolic verifier (Entries 40-43): word-boundary
boolean matching, comparator refusal guard, and a global refusal guard so a text-only
refusal ("cannot determine", "without knowing", "no definitive answer") scores
UNEXTRACTABLE for all slot types. A question-only prior cannot earn a lucky PASS from
keywords inside its refusal text.

## Evaluation protocol (Stage 9 VLM)

Per the project design, report for EACH model:

1. **Per-category accuracy** (morphology, counting, localization, comparison,
   existence_detection, multi_hop) — not just overall accuracy.
2. **Hallucination-type breakdown** — counting / spatial / shape / relational /
   unsupported-statement, via the mismatch classifier on extracted slots.
3. **Accuracy vs. difficulty** — correlation with `difficulty` (program depth), the
   empirical backing for the DSL depth metric.
4. **Organic-vs-composited split** — CVC/ETIS comparison & multi-hop items reported
   separately for organic vs. `etis-composites` subsets (artifact detector labels each
   ETIS item; composite detectability must be disclosed).
5. **Calibration** — confidence vs. correctness.

**Model disjointness constraint:** all Stage 9 VLMs must be disjoint from the
`Qwen2.5-7B-Instruct` text-only model used to generate the questions/answers in Stage 3.
This avoids the circularity already controlled for in the B1/B1b question-only baselines.

## Provenance & license

- **Kvasir-SEG** (Jha et al., MICCAI 2020, CC BY 4.0) — 345 images in the benchmark subset.
- **CVC-ClinicDB** (Bernal et al., 2015) — 283 images.
- **ETIS-LaribPolypDB** (Silva et al., 2014) — 132 organic + 133 composite images.
- Questions, answers, programs, traces, and `verified` labels were produced by the SegVQA
  pipeline (DSL sampling → LLM paraphrase → independent symbolic verifier). Composite
  images are synthetic multi-lesion composites for quota filling; the artifact detector
  reports 0.944 accuracy distinguishing composited vs. organic frames (transparency check).

## Quick start (other-server eval)

```python
from datasets import load_dataset
ds = load_dataset("AiventraLab/segvqa-benchmark", split="train")
# image for item i:
img = ds[i]["file_name"]
# to load the actual image:
from PIL import Image
im = Image.open(f"https://huggingface.co/datasets/AiventraLab/segvqa-benchmark/resolve/main/{img}")
```

Or `git lfs pull` / `hf download AiventraLab/segvqa-benchmark` to mirror the whole dataset.