README.md

metadata

language:
  - en
license: apache-2.0
task_categories:
  - visual-question-answering
  - image-classification
  - image-to-text
pretty_name: FineSightBench
size_categories:
  - 1K<n<10K
tags:
  - VLM-evaluation
  - fine-grained-visual-perception
  - fine-grained-visual-reasoning
  - text-in-the-wild
  - scene-text-recognition
splits:
  - name: perception
    num_examples: 4200
  - name: reasoning
    num_examples: 3920

FineSightBench

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 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

perception split — 4 200 samples

Single-target identification tasks. 700 samples per task, 100 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 — 3 920 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

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

from datasets import load_dataset

ds = load_dataset("Volavion/FineSightBench")
print(ds)
# DatasetDict({
#     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"])      # JSON string, e.g. '{"letter": "A"}'

Generation

Generated with the FineSightBench repository:

python generate.py perception --output data/full_perception
python generate.py reasoning  --output data/full_reasoning

Citation

If you use FineSightBench, please cite the repository and the SynthText background source:

@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}
}