ImageNet-1K-Camera

Per-image camera parameter annotations for the full ImageNet-1K dataset (1,000 training classes + the 50,000-image validation split, ~1.35M images), captioned by the Puffin-World model.

The collage above visualizes the camera maps on sample images — each pair shows the up field (green arrows: the projected gravity-up direction) and the latitude field (colored contours: angle above/below the horizon).

Format

The archive mirrors the source ImageNet WebDataset layout: one .tar per source shard (n*.tar per class, plus val.tar), each containing one .json per image whose name matches the source image stem.

Each JSON holds the predicted monocular camera parameters:

Example:

{"roll": 0.0123, "pitch": -0.0871, "vfov": 1.0123, "k1": 0.0000, "parse_ok": true}

Camera Parameter Distributions

Histograms of the predicted roll / pitch / vertical-FoV, train and val plotted separately (proportion of valid samples per 10° bin; parse_ok=False excluded).

Train (1,278,950 images)

Val (49,924 images)

• Roll is sharply peaked at 0° (images shot upright/level).
• Pitch is slightly negative (a mild downward-looking tendency).
• FoV concentrates in 20–40° (median ≈ 26–28°); train and val agree closely.

Dataset Download

You can download the entire dataset using the following command:

hf download KangLiao/ImageNet-1K-Camera --repo-type dataset

Caption Pipeline

Beyond this captioned dataset, we also release a complete captioning pipeline for annotating camera parameters for arbitrary datasets, analyzing camera parameter distributions, and visualizing the corresponding camera maps. The pipeline is available in our GitHub repository.

Citation

If you find the captioned dataset useful for your research or applications, please cite our paper using the following BibTeX:

  @article{liao2025puffin,
    title={Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation},
    author={Liao, Kang and Wu, Size and Wu, Zhonghua and Jin, Linyi and Wang, Chao and Wang, Yikai and Wang, Fei and Li, Wei and Loy, Chen Change},
    journal={arXiv preprint arXiv:2510.08673},
    year={2025}
  }