google-research-datasets/Objectron resource
Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes observed · 2026-08-28
Health v2 · maintenance only
54/100
- Activity 70
- Release rhythm 8
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2144
- days_rel: n/a
- days_push: 180
- n_releases_24m: 0
Adoption not part of the score
2349 stars · 266 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Objectron is a dataset of ~15K object-centric video clips with over 4M annotated images, including 3D bounding box annotations, camera poses, sparse point-clouds, and plane metadata. It ships with parsing tools and tutorials for TensorFlow and PyTorch, covering nine object categories such as bikes, books, bottles, cameras, chairs, cups, laptops, and shoes.
Use cases
- train 3D object detection models from video
- get 3D bounding box annotations for everyday objects
- research multi-view object pose estimation
- benchmark 3D IoU metrics for oriented bounding boxes
- build augmented reality object tracking features
- download annotated AR session data with camera poses and point clouds
When to choose
- you need labeled 3D bounding boxes for object detection research
- you want multi-view video data with AR metadata like camera poses and planes
- you need ready-made tf.record pipelines for TensorFlow or PyTorch
When to avoid
- you need 2D-only image classification labels
- you require objects outside the nine supported categories
- you need real-time inference rather than training data
Facets
dataset · maturity stable
machine-learning computer-vision data-science computer-vision machine-learning deep-learning artificial-intelligence python cross-platform 3d-object-detection 3d-bounding-boxes augmented-reality video-dataset pose-estimation tensorflow pytorch 3d-vision
1 source
- readme: https://github.com/google-research-datasets/Objectron · fetched 2026-08-28 · c0f02fea7dae
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| google-research-datasets/Objectron | main | 54 |
For agents
markdown · JSON · MCP: product_card(name="google-research-datasets/Objectron")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem