| --- |
| 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. |
|
|
