🕐 TickTockVQA

Analog clock reading dataset from It's Time to Get It Right: Improving Analog Clock Reading and Clock-Hand Spatial Reasoning in Vision-Language Models (CVPR 2026 Findings). Contains 12,483 images with time labels for training and evaluating VQA models on analog clock understanding.

Dataset Notice: This dataset is collected from publicly available data corpora. The copyright and redistribution conditions of the dataset do not belong to the authors of this project. Please refer to the respective source data corpora and the license information in the annotations.json file for details on usage, attribution, and redistribution terms.

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📖 Overview

TickTockVQA is a benchmark dataset for reading analog clocks from images. Each sample includes an image and the ground-truth time displayed on the most prominent clock face.

Split	Samples
Train	7,236
Test	5,247

Sources: OpenImages, COCO, ClockMovies, VisualGenome, CC12M, ImageNet, SBU

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📊 Qualitative Results

Sample predictions and visualizations from our models on the TickTockVQA benchmark.

Effect of Swap-DPO

Qualitative examples of hand-swap error correction by Swap-DPO. SFT incorrectly swaps the hour and minute hands, whereas Swap-DPO successfully corrects this systematic error pattern.

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📁 Files

File	Description
annotations.json	Image annotations with image_name, image_path, time_string, hour, minute, license info
qualitative_results.png	Qualitative results (sample predictions and visualizations)
handswap_qualitative_results.png	Hand-swap error correction by Swap-DPO
dataset_statistics.json	Dataset statistics (source distribution, splits, license distribution)

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🚀 Quick Start

from datasets import load_dataset
dataset = load_dataset("jaeha-choi/TickTockVQA")

Or download annotations only:

huggingface-cli download jaeha-choi/TickTockVQA annotations.json --local-dir ./data

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📚 Citation

If you find our work useful, please cite:

@article{choi2026clockreasoning,
  title   = {It's Time to Get It Right: Improving Analog Clock Reading and Clock-Hand Spatial Reasoning in Vision-Language Models},
  author  = {Choi, Jaeha and Lee, Jin Won and You, Siwoo and Lee, Jangho},
  journal = {arXiv preprint arXiv:2603.08011},
  year    = {2026},
  url     = {https://arxiv.org/abs/2603.08011}
}

If you find our work useful, we would appreciate a Hugging Face 🤗 like and a GitHub ⭐ Star. Thank you!

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🔗 Links

• GitHub: https://github.com/allchiever/It-s-Time-to-Get-It-Right
• Project Page: https://it-s-time-to-get-it-right.github.io/
• arXiv: https://arxiv.org/abs/2603.08011