Datasets:
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pretty_name: Anonymous Visual Perception Benchmark
task_categories:
- image-text-to-text
language:
- zh
- en
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: test
path: data/test-*.parquet
---
# Anonymous Visual Perception Benchmark
This evaluation dataset contains 2,876 image-question pairs with Chinese and
English questions, reference answers, and hierarchical visual-perception labels.
Images are embedded in Parquet files; no external image service is required.
## Data Format
The dataset has one `test` split. Each row contains:
| Field | Description |
| --- | --- |
| `id` | Release-local sample identifier, unrelated to internal identifiers. |
| `image` | Embedded PNG image, decoded as a Hugging Face `Image` feature. |
| `question_zh`, `question_en` | Chinese and English question text. |
| `answer_zh`, `answer_en` | Chinese and English reference answers. |
| `answer_mask` | JSON-encoded geometric annotations where provided; otherwise an empty string. |
| `l1_zh` through `l4_zh` | Chinese hierarchical category labels. |
| `l1_en` through `l4_en` | Corresponding English category labels. |
The seven top-level categories are Grounding, Semantic Recognition,
Fine-grained Discrimination, Visual Tracking, Space Understanding, OCR, and
Structured Visual Perception. There are 21 distinct top-level/subcategory pairs.
The OCR portion includes 300 questions labeled Text Recognition in Less Common
Languages.
The original annotation text is preserved. Some lower-level labels are absent:
7 rows have an empty third-level label and 75 rows have an empty fourth-level
label. Empty annotations are not inferred or fabricated.
## Loading
```python
from datasets import load_dataset
dataset = load_dataset("Susan0803/visual-perception-benchmark-anon", split="test")
sample = dataset[0]
image = sample["image"]
question = sample["question_en"]
reference_answer = sample["answer_en"]
```
To load a local checkout, use:
```python
dataset = load_dataset("parquet", data_files={"test": "data/test-*.parquet"}, split="test")
```
## Release Notes
Only questions, reference annotations, images, and category labels are included.
Model predictions, model identifiers, evaluation scores, reviewer notes,
internal storage addresses, and internal sample identifiers are excluded.
Images are stored as self-contained PNGs without embedded EXIF or text metadata.
Image orientation is applied to pixels, and embedded color profiles are converted
to sRGB. Multi-picture JPEG containers are represented by their primary image.
This removes file metadata, not text visibly present within benchmark images.
The images themselves are evaluation content and are not redacted.
This repository does not assert an additional license over third-party image
content. Public accessibility alone does not establish redistribution rights.
|