README.md

---
license: cc-by-4.0
pretty_name: Amazon Berkeley Objects
language:
- en
- de
- es
- fr
- it
- ja
- ko
- pt
- zh
task_categories:
- image-classification
- image-to-3d
- object-detection
tags:
- 3d
- mesh
- glb
- product
- ecommerce
- multi-view
- material
- amazon-berkeley-objects
configs:
- config_name: objects
  default: true
  data_files:
  - split: train
    path: objects/train-*.parquet
- config_name: listings
  data_files:
  - split: train
    path: listings/train-*.parquet
- config_name: images_small
  data_files:
  - split: train
    path: images_small/train-*.parquet
- config_name: images_original
  data_files:
  - split: train
    path: images_original/train-*.parquet
- config_name: spins
  data_files:
  - split: train
    path: spins/train-*.parquet
- config_name: models_3d
  data_files:
  - split: train
    path: models_3d/train-*.parquet
- config_name: part_labels
  data_files:
  - split: train
    path: part_labels/train-*.parquet
  - split: validation
    path: part_labels/validation-*.parquet
  - split: test
    path: part_labels/test-*.parquet
- config_name: benchmark_material
  data_files:
  - split: train
    path: benchmark_material/train-*.parquet
  - split: test
    path: benchmark_material/test-*.parquet
- config_name: mvr
  data_files:
  - split: train
    path: mvr/train-*.parquet
  - split: validation
    path: mvr/validation-*.parquet
  - split: test
    path: mvr/test-*.parquet
---

# Amazon Berkeley Objects

This repository is a viewer-compatible Hugging Face packaging of the official
[Amazon Berkeley Objects (ABO)](https://amazon-berkeley-objects.s3.us-east-1.amazonaws.com/index.html)
release. ABO contains 147,702 multilingual product listings, 398,212 unique
catalog images, 586,584 turntable images, 7,953 high-quality glTF 2.0 models,
material-benchmark renders and geometry buffers, fine-grained 3D part labels,
and the official multi-view retrieval splits.

The data content is the official CC BY 4.0 release. This mirror changes only
the packaging: metadata and media are grouped into typed Parquet shards so the
Hugging Face Dataset Viewer and `datasets` streaming APIs can consume them.
No claim of endorsement by Amazon or UC Berkeley is made.

## Recommended entry point

The default `objects` config is the product-level index and the easiest way to
browse ABO. Each row joins a listing to its 256 px main image, native GLB mesh
when available, and flags/foreign keys for spins, part labels, and material
benchmark records.

```python
from datasets import load_dataset

objects = load_dataset(
    "suvadityamuk/amazon-berkeley-objects",
    "objects",
    split="train",
    streaming=True,
)
first = next(iter(objects))
print(first["item_id"], first["item_name"], first["has_3d_model"])
```

Use a modality config for complete training data:

```python
images = load_dataset(
    "suvadityamuk/amazon-berkeley-objects",
    "images_small",
    split="train",
    streaming=True,
)
models = load_dataset(
    "suvadityamuk/amazon-berkeley-objects",
    "models_3d",
    split="train",
    streaming=True,
)
```

Native 3D preview uses the Hugging Face `Mesh()` feature and therefore requires
a recent `datasets` release with mesh support.

## Configs

| Config | Row unit | Main fields |
|---|---|---|
| `objects` | One product | Listing summary, `Image()` main image, optional `Mesh()` GLB, modality flags |
| `listings` | One product listing | Display fields plus lossless `raw_listing_json` |
| `images_small` | One catalog image | 256 px `Image()`, image id, dimensions, official path |
| `images_original` | One catalog image | Original-resolution `Image()`, image id, dimensions, official path |
| `spins` | One turntable frame | `Image()`, spin id, image id, azimuth |
| `models_3d` | One 3D product | Native `Mesh()` GLB and geometry/material statistics |
| `part_labels` | One connected/convex part mesh | Original OBJ bytes, converted `Mesh()` GLB preview, semantic label and relations |
| `benchmark_material` | One model viewpoint | Base color, metallic/roughness, normal, segmentation and three render `Image()` columns; EXR depth bytes; camera data |
| `mvr` | One retrieval example | Official class/group/image ids, source path, metadata and split |

The `source_metadata/` directory retains the official top-level README,
download URL list, compressed metadata tables, benchmark split files, and
sample indices.

### Format notes

- JPEG and PNG fields use `Image()` and render in the Dataset Viewer.
- Official GLB files use `Mesh()` and render in the 3D viewer.
- Part-label OBJ files are preserved byte-for-byte in `source_obj`; an
  additional GLB conversion in `mesh` enables browser preview.
- OpenEXR depth maps are preserved byte-for-byte in `depth_exr`. EXR does not
  have a native Hub image preview, so each row also exposes its corresponding
  JPEG/PNG render and material maps.
- Empty strings and `-1` labels indicate fields not supplied by the official
  source. A non-empty `conversion_error` means an OBJ was preserved but could
  not be converted for mesh preview.

## Source dataset sizes

The extracted official release used for this mirror occupies approximately
599 GiB:

- listings: 84 MiB
- small catalog images: 3.6 GiB
- original catalog images: 111 GiB
- spin frames: 41 GiB
- 3D models: 155 GiB
- part labels: 11 GiB
- material benchmark: 277 GiB

Typed Parquet configs can be larger in aggregate because `objects` embeds
preview media already present in modality-specific configs and part labels
contain both source OBJ and preview GLB representations.

## License

The official release is licensed under the
[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
The complete license text is included as
[`LICENSE-CC-BY-4.0.txt`](LICENSE-CC-BY-4.0.txt).

You must give appropriate credit, link to the license, and indicate whether
changes were made. You may not imply endorsement by the licensors.

## Required attribution

The official archive README requires the following credit:

> 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), Jasmine Collins
> (UC Berkeley), and Jitendra Malik (UC Berkeley).

The current official project page additionally credits Arnab Dhua,
Xi (Brian) Zhang, Tomas Yago-Vicente, and Shubham Goel.

## Citation

Please cite the official CVPR 2022 paper when using ABO:

```bibtex
@InProceedings{Collins_2022_CVPR,
  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},
  title     = {ABO: Dataset and Benchmarks for Real-World 3D Object Understanding},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and
               Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2022},
  pages     = {21126--21136}
}
```

The fine-grained part labels were released for the
[3D Vision and Modeling Challenges in eCommerce, ICCV 2023 workshop challenge](https://3dv-in-ecommerce.github.io/).

## Official resources

- [ABO project and downloads](https://amazon-berkeley-objects.s3.us-east-1.amazonaws.com/index.html)
- [CVPR 2022 paper](https://openaccess.thecvf.com/content/CVPR2022/html/Collins_ABO_Dataset_and_Benchmarks_for_Real-World_3D_Object_Understanding_CVPR_2022_paper.html)
- [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)