license: cc-by-4.0
pretty_name: Amazon Berkeley Objects (ABO)
source_datasets:
- original
task_categories:
- image-classification
- image-to-3d
tags:
- amazon
- products
- 3d
- images
- ecommerce
- abo
configs:
- config_name: mvr
data_files:
- split: train
path: mvr/train-*.parquet
- config_name: listings
data_files:
- split: train
path: listings/train-*.parquet
- config_name: part_labels
data_files:
- split: train
path: part_labels/train-*.parquet
- config_name: images_small
data_files:
- split: train
path: images_small/train-*.parquet
- config_name: spins
data_files:
- split: train
path: spins/train-*.parquet
- config_name: images_original
data_files:
- split: train
path: images_original/train-*.parquet
- config_name: models_3d
data_files:
- split: train
path: models_3d/train-*.parquet
- config_name: benchmark_material
data_files:
- split: train
path: benchmark_material/train-*.parquet
- config_name: objects
default: true
data_files:
- split: train
path: objects/train-*.parquet
Amazon Berkeley Objects (ABO)
A Hugging Face packaging of the Amazon Berkeley Objects (ABO) dataset. The
data content is the official CC BY 4.0 release from
https://amazon-berkeley-objects.s3.amazonaws.com/index.html. This mirror
changes only the packaging: metadata and media are grouped into typed Parquet
shards using the datasets Image() and Mesh() features so the Hugging Face
Dataset Viewer and datasets streaming APIs can consume them.
Configs
| Config | Row unit | Main fields |
|---|---|---|
objects (default) |
One product | Listing summary, foreign keys, modality flags |
listings |
One product listing | Typed fields plus lossless raw_listing_json |
images_small |
One catalog image | 256 px Image(), image_id, dimensions, path |
images_original |
One catalog image | Original-resolution Image(), dimensions, path |
spins |
One turntable frame | Image(), spin_id, image_id, azimuth |
models_3d |
One 3D product | Native Mesh() GLB and geometry/material stats |
part_labels |
One part mesh | Byte-exact source_obj plus converted Mesh() GLB |
benchmark_material |
One model-viewpoint file | Render/material Image() or EXR blob, kind |
mvr |
One retrieval example | Official CVPR22 multi-view-retrieval split columns |
Native 3D preview uses the Mesh() feature and requires a recent datasets
release with mesh support.
Licensing
This work is licensed under the Creative Commons Attribution 4.0 International
Public License (CC BY 4.0). A verbatim copy is provided in
LICENSE-CC-BY-4.0.txt; see also
https://creativecommons.org/licenses/by/4.0/.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Changes made: the original release was repackaged into typed Parquet shards
(Image()/Mesh() features) for the Hugging Face Dataset Viewer and
datasets. No underlying data content was altered.
Note: the source S3 bucket root also contains a
LICENSE-CC-BY-NC-4.0.txtfile, but the official ABO download page licenses the released archives under CC BY 4.0, which is the license applied and mirrored here.
Attribution
Credit for the data, including all images and 3D models, must be given to:
Amazon.com
Credit for building the dataset, archives and benchmark sets must be given to:
Matthieu Guillaumin (Amazon.com), Thomas Dideriksen (Amazon.com), Kenan Deng (Amazon.com), Himanshu Arora (Amazon.com), Arnab Dhua (Amazon.com), Xi (Brian) Zhang (Amazon.com), Tomas Yago-Vicente (Amazon.com), Jasmine Collins (UC Berkeley), Shubham Goel (UC Berkeley), Jitendra Malik (UC Berkeley).
No endorsement by Amazon.com or UC Berkeley of this mirror is claimed or implied.
Citation
@article{collins2022abo,
title={ABO: Dataset and Benchmarks for Real-World 3D Object Understanding},
author={Collins, Jasmine and Goel, Shubham and Deng, Kenan and Luthra, Achleshwar and
Xu, Leon and Gundogdu, Erhan and Zhang, Xi and Yago Vicente, Tomas F and
Dideriksen, Thomas and Arora, Himanshu and Guillaumin, Matthieu and
Malik, Jitendra},
journal={CVPR},
year={2022}
}