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. More captioned datasets are provided in our Puffin-16M website.

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:

Field	Meaning	Unit
roll	camera roll	radians
pitch	camera pitch	radians
vfov	vertical field-of-view	radians
k1	radial distortion coefficient	–
parse_ok	whether the model output parsed within valid ranges	bool

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)

split	roll μ / med / σ	pitch μ / med / σ	FoV μ / med / σ
train	0.1° / 0.0° / 7.8°	−7.8° / −3.7° / 15.7°	28.9° / 25.6° / 8.9°
val	0.1° / 0.0° / 8.5°	−8.8° / −4.5° / 16.7°	30.3° / 27.5° / 9.1°

• 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.

If you'd like a dataset with a more diverse and uniform distribution of camera parameters, please refer to our Puffin-4M and Puffin-16M datasets.

Dataset Download

You can download the entire dataset using the following command:

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

From Camera Parameters to Up and Latitude Fields

The released (roll, pitch, vfov, k1) annotations can be converted into the dense perspective-field representation used by Puffin-World. The conversion computes focal length from vfov, constructs a radial camera, and maps roll and pitch to the gravity direction. get_perspective_field then returns a normalized 2-channel up field and a 1-channel latitude field. The up field encodes the projected world-up direction at every pixel, while the latitude field measures each viewing ray's angular elevation relative to the horizon.

import json
import torch
from scripts.camera.geometry.camera import SimpleRadial
from scripts.camera.geometry.gravity import Gravity
from scripts.camera.geometry.perspective_fields import get_perspective_field
from scripts.camera.utils.conversions import fov2focal

with open("camera.json") as f:
    annotation = json.load(f)
roll, pitch, vfov, k1 = (
    annotation[key] for key in ("roll", "pitch", "vfov", "k1")
)

H, W = 512, 512
f = float(fov2focal(torch.tensor(vfov), H))
camera = SimpleRadial(torch.tensor(
    [W, H, f, f, W / 2, H / 2, k1, 0.0]
).float()).scale(torch.tensor([1.0, 1.0]))
gravity = Gravity.from_rp(torch.tensor(roll), torch.tensor(pitch))
up_field, latitude_field = get_perspective_field(camera, gravity)
# Shapes: [1, 2, H, W] and [1, 1, H, W]

Run the example from the Puffin-World directory. Use plot_vector_fields and plot_latitudes to render up-field arrows and latitude heatmaps or contours. See save_pf_visualization for an end-to-end visualization example.

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 the following papers using these BibTeX entries:

  @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}
  }

  @article{liao2026puffinworld,
    title={Puffin-World: Scaling a Unified Multimodal Model with Native 3D World States},
    author={Liao, Kang and Luo, Yihang and Wu, Xiao-Ming and Jin, Linyi and Wu, Size and Lin, Chunyu and Zhao, Yao and Wang, Fei and Li, Wei and Loy, Chen Change},
    journal={arXiv preprint arXiv:2609.04196},
    year={2026}
  }