Add dataset card for FineSightBench-Large v1.0

README.md

@@ -1,34 +1,171 @@
 ---
-configs:
-- config_name: default
-  data_files:
-  - split: perception
-    path: data/perception-*
-  - split: reasoning
-    path: data/reasoning-*
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: image_id
-    dtype: string
-  - name: task_type
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: difficulty
-    dtype: string
-  - name: metadata
-    dtype: string
-  splits:
+language:
+  - en
+license: apache-2.0
+task_categories:
+  - visual-question-answering
+  - image-classification
+  - image-to-text
+pretty_name: FineSightBench-Large
+size_categories:
+  - 10K<n<100K
+tags:
+  - VLM-evaluation
+  - fine-grained-visual-perception
+  - fine-grained-visual-reasoning
+  - text-in-the-wild
+  - scene-text-recognition
+splits:
   - name: perception
-    num_bytes: 87693559
     num_examples: 42000
   - name: reasoning
-    num_bytes: 126526373
     num_examples: 39200
-  download_size: 7116750355
-  dataset_size: 214219932
 ---
+
+# FineSightBench-Large
+
+**FineSightBench-Large** is a **10× scaled** edition of [FineSightBench](https://huggingface.co/datasets/Volavion/FineSightBench) — identical task design, difficulty sweep, answer schemas, and image regimes, with every base sample count multiplied by ten for higher statistical power and robust per-(task, size, count) evaluation.
+
+**FineSightBench** is a fine-grained visual benchmark for evaluating Vision-Language Models (VLMs) on pixel-level perception and reasoning tasks. It combines two complementary image regimes:
+
+1. **Synthetic canvas** — controlled white-background images with precisely-sized geometric/semantic targets (letters, animals, shapes, blocks, dots).
+2. **Text in the wild** (SynthText-style) — English words rendered onto real natural-scene photographs from the [SynthText](https://github.com/ankush-me/SynthText) `bg_img` set, with **pixel-accurate control of character cap-height**.
+
+All images are **448 × 448 px**. The primary difficulty axis is the **target pixel size** (cap-height for text), swept over `[4, 8, 12, 16, 24, 32, 48]`, mapped to `extreme / hard / medium / easy`.
+
+## Dataset Summary
+
+| Split | #Samples | #Task types | Regimes |
+|-------|---------:|:-----------:|---------|
+| `perception` | 42 000 | 6 | synthetic canvas + text-in-the-wild |
+| `reasoning`  | 39 200 | 6 | synthetic canvas + text-in-the-wild |
+
+## Dataset Structure
+
+### `perception` split — 42 000 samples
+
+Single-target identification tasks. 7 000 samples per task, 1 000 samples per pixel size × 7 sizes.
+
+| `task_type` | Description | Source |
+|-------------|-------------|--------|
+| `letter_recognition` | Identify a rendered uppercase letter (A–Z) | synthetic canvas |
+| `animal_recognition` | Identify an animal silhouette (cat/dog/fish/bird/rabbit/turtle) | synthetic canvas |
+| `shape_recognition` | Identify a geometric shape (circle/triangle/square/star/diamond/pentagon/hexagon/cross) | synthetic canvas |
+| `block_recognition` | Detect / count square blocks | synthetic canvas |
+| `color_block_recognition` | Identify the color of a block | synthetic canvas |
+| `text_recognition` | Read a single English word overlaid on a natural scene | **text in the wild** |
+
+### `reasoning` split — 39 200 samples
+
+Chain-reasoning tasks requiring counting, ordering, and spatial reasoning across multiple targets.
+
+| `task_type` | Description | Source |
+|-------------|-------------|--------|
+| `spatial_chain` | List all objects left→right or top→bottom | synthetic canvas |
+| `comparison_chain` | List all objects smallest→largest by size | synthetic canvas |
+| `counting_chain` | Count objects per type + total | synthetic canvas |
+| `blur_chain` | Count objects on a blurred/textured background | synthetic canvas |
+| `text_reading_chain` | Read multiple overlaid words in left→right / top→bottom order | **text in the wild** |
+| `text_counting_chain` | Total word count + # words containing a queried letter | **text in the wild** |
+
+### Difficulty levels
+
+| Difficulty | Target / cap-height |
+|------------|---------------------|
+| `extreme`  | ≤ 5 px |
+| `hard`     | 6–12 px |
+| `medium`   | 13–24 px |
+| `easy`     | 25–48 px |
+
+## Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `image`      | Image    | 448×448 PNG |
+| `image_id`   | string   | Unique identifier (encodes task, size, count) |
+| `task_type`  | string   | See tables above |
+| `question`   | string   | Prompt for the VLM (asks for a structured JSON answer) |
+| `answer`     | string   | Ground-truth answer. JSON-encoded (see below) |
+| `difficulty` | string   | `easy` / `medium` / `hard` / `extreme` |
+| `metadata`   | string   | JSON with canvas size, target pixel size, positions, colors, bounding boxes, sub-answers, etc. |
+
+### Answer schemas (examples)
+
+| Task | Answer JSON |
+|------|-------------|
+| `letter_recognition` | `{"letter": "H"}` |
+| `animal_recognition` | `{"animal": "rabbit"}` |
+| `shape_recognition`  | `{"shape": "triangle"}` |
+| `color_block_recognition` | `{"color": "blue"}` |
+| `text_recognition`   | `{"word": "HOME"}` |
+| `spatial_chain`      | `{"objects": ["red A", "blue K", ...]}` |
+| `comparison_chain`   | `{"objects": ["blue dog", "magenta bird"]}` |
+| `counting_chain`     | `{"counts": {"red": 2, "blue": 1}, "total": 3}` |
+| `blur_chain`         | `{"counts": {"circle": 1, "square": 2}, "total": 3}` |
+| `text_reading_chain` | `{"words": ["HOME", "CITY", "EXIT"]}` |
+| `text_counting_chain`| `{"total": 6, "with_letter": 3}` |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Volavion/FineSightBench-Large")
+print(ds)
+# DatasetDict({
+#     perception: Dataset({features: [...], num_rows: 42000}),
+#     reasoning:  Dataset({features: [...], num_rows: 39200})
+# })
+
+sample = ds["perception"][0]
+sample["image"].show()
+print(sample["question"])
+print(sample["answer"])      # JSON string, e.g. '{"letter": "A"}'
+```
+
+Filter by task or difficulty:
+
+```python
+text_subset = ds["perception"].filter(lambda x: x["task_type"] == "text_recognition")
+extreme    = ds["perception"].filter(lambda x: x["difficulty"] == "extreme")
+```
+
+## Design Philosophy
+
+* **Pixel-size is the primary difficulty axis.** Targets (objects or characters) are rendered at exact cap-heights across `[4, 8, 12, 16, 24, 32, 48]` px so that the same semantic task can be probed from *easily readable* to *near-imperceptible* scales on a single fixed 448×448 canvas.
+* **Controlled composition.** Every sample exposes pixel-precise target positions, bounding boxes, colors (with RGB), and sub-answers in `metadata`, enabling per-task, per-size, per-color, and positional analyses.
+* **Two image regimes.** The synthetic canvas removes distribution confounders, while the SynthText-style text-in-the-wild regime stresses models with the same text task on varied, real photographs.
+
+## Generation
+
+Generated with the [FineSightBench repository](https://github.com/Volavion/FineSightBench):
+
+```bash
+# 10× base counts (perception: --num-per-config 1000, reasoning: N_PER_CONFIG=200)
+python scripts/generate_large_dataset.py   # FSB_LARGE_SCALE=10 by default
+```
+
+**Text-in-the-wild backgrounds**: the first ~1 500 JPEGs from the SynthText `bg_img.tar.gz` set ([mirror](https://thor.robots.ox.ac.uk/scenetext/preproc/bg_img.tar.gz)) are center-cropped and resized to 448×448. Text glyphs use system sans-serif fonts; cap-height is calibrated per render to match the requested pixel size exactly.
+
+## Citation
+
+If you use FineSightBench, please cite the repository and the SynthText background source:
+
+```bibtex
+@misc{finesightbench_large2026,
+  title  = {FineSightBench-Large: 10	imes Scaled Fine-grained Visual Perception \& Reasoning Benchmark for VLMs},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/Volavion/FineSightBench-Large}
+}
+
+@inproceedings{Gupta16,
+  author    = {A. Gupta and A. Vedaldi and A. Zisserman},
+  title     = {Synthetic Data for Text Localisation in Natural Images},
+  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
+  year      = {2016}
+}
+```
+
+## License
+
+Apache-2.0 for the FineSightBench benchmark code, annotations, and synthetic images. The natural-scene backgrounds for the text-in-the-wild tasks are derived from the SynthText `bg_img` set; please refer to the original SynthText dataset for the background-image license/terms.