Add dataset card (v1.1 with text-in-the-wild tasks)

README.md

@@ -1,98 +1,107 @@
 ---
 language:
-- en
+  - en
 license: apache-2.0
 task_categories:
-- visual-question-answering
-- image-classification
+  - visual-question-answering
+  - image-classification
+  - image-to-text
 pretty_name: FineSightBench
 size_categories:
-- 1K<n<10K
+  - 1K<n<10K
 tags:
-- VLM-evaluation
-- fine-grained-visual-perception
-- fine-grained-visual-reasoning
+  - VLM-evaluation
+  - fine-grained-visual-perception
+  - fine-grained-visual-reasoning
+  - text-in-the-wild
+  - scene-text-recognition
 splits:
-- name: perception
-  num_examples: 3500
-- name: reasoning
-  num_examples: 2520
-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:
   - name: perception
-    num_bytes: 9514691
     num_examples: 4200
   - name: reasoning
-    num_bytes: 16327640
     num_examples: 3920
-  download_size: 707864892
-  dataset_size: 25842331
-configs:
-- config_name: default
-  data_files:
-  - split: perception
-    path: data/perception-*
-  - split: reasoning
-    path: data/reasoning-*
 ---
 
 # FineSightBench
 
-FineSightBench is a fine-grained visual benchmark for evaluating Vision Language Models (VLMs) on pixel-level perception and reasoning tasks.
+**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` | 4 200 | 6 | synthetic canvas + text-in-the-wild |
+| `reasoning`  | 3 920 | 6 | synthetic canvas + text-in-the-wild |
 
 ## Dataset Structure
 
-The dataset consists of two splits:
+### `perception` split — 4 200 samples
 
-### `perception` (3 500 images)
+Single-target identification tasks. 700 samples per task, 100 samples per pixel size × 7 sizes.
 
-Fine-grained single-target identification tasks at 8 pixel-size difficulty levels.
+| `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** |
 
-| Task | Description |
-|------|-------------|
-| Color Identification | Identify the color of a target object |
-| Letter Recognition | Identify a rendered letter |
-| Animal Recognition | Identify an animal silhouette |
-| Shape Recognition | Identify a geometric shape |
-| Dot Color Recognition | Identify the color of a tiny dot |
+### `reasoning` split — 3 920 samples
 
-### `reasoning` (2 520 images)
+Chain-reasoning tasks requiring counting, ordering, and spatial reasoning across multiple targets.
 
-Chain-reasoning tasks requiring counting, ordering, and spatial reasoning.
+| `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** |
 
-| Task | Description |
-|------|-------------|
-| `spatial_chain` | List objects left→right or top→bottom |
-| `comparison_chain` | List objects smallest→largest by size |
-| `counting_chain` | Count objects per type + total |
-| `blur_chain` | Count objects on blurred background |
+### 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 canvas |
-| `image_id` | string | Unique identifier |
-| `task_type` | string | Task category |
-| `question` | string | Prompt for the VLM |
-| `answer` | string | Ground-truth answer (JSON string for reasoning) |
-| `difficulty` | string | `easy` / `medium` / `hard` / `extreme` |
-| `metadata` | string | JSON with canvas_size, pixel_size, targets list |
+| `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
 
@@ -102,17 +111,60 @@ from datasets import load_dataset
 ds = load_dataset("Volavion/FineSightBench")
 print(ds)
 # DatasetDict({
-#     perception: Dataset({features: [...], num_rows: 3500}),
-#     reasoning:  Dataset({features: [...], num_rows: 2520})
+#     perception: Dataset({features: [...], num_rows: 4200}),
+#     reasoning:  Dataset({features: [...], num_rows: 3920})
 # })
 
 sample = ds["perception"][0]
 sample["image"].show()
 print(sample["question"])
-print(sample["answer"])
+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
+python generate.py perception --output data/full_perception
+python generate.py reasoning  --output data/full_reasoning
+python generate.py textwild   --split all --output data   # merges text-in-the-wild tasks
+```
+
+**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{finesightbench2026,
+  title  = {FineSightBench: Fine-grained Visual Perception \& Reasoning Benchmark for VLMs},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/Volavion/FineSightBench}
+}
+
+@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}
+}
 ```
 
-## Canvas Design
+## License
 
-All images are 448×448 pixels on a white background.  
-Object pixel sizes range from **3 px** (extreme) to **48 px** (easy), controlling task difficulty.
+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.