| --- |
| 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. |
|
|
