---
license: cc-by-4.0
pretty_name: Amazon Berkeley Objects
tags:
- computer-vision
- image-retrieval
- product-catalog
- hierarchy
- 3d
configs:
- config_name: listings
  data_files:
  - split: train
    path: data/listings/*.parquet
- config_name: images
  data_files:
  - split: train
    path: data/images/*.parquet
- config_name: spins
  data_files:
  - split: train
    path: data/spins/*.parquet
- config_name: 3dmodels
  data_files:
  - split: train
    path: data/3dmodels/*.parquet
---

# Amazon Berkeley Objects

This is a Hugging Face metadata mirror of the Amazon Berkeley Objects dataset
for reproducible research and HyperView demos. The original dataset is provided
by Amazon.com and UC Berkeley.

This mirror stores metadata tables and official S3 asset URLs. It does not
duplicate catalog images, turntable images, or 3D models as binary files.

## Load

```python
from datasets import load_dataset

listings = load_dataset("hyper3labs/amazon-berkeley-objects", "listings", split="train")
images = load_dataset("hyper3labs/amazon-berkeley-objects", "images", split="train")
spins = load_dataset("hyper3labs/amazon-berkeley-objects", "spins", split="train")
models = load_dataset("hyper3labs/amazon-berkeley-objects", "3dmodels", split="train")
```

## Tables

- `listings`: all product listings with normalized hierarchy columns and
  `raw_listing_json` preserving the original listing object.
- `images`: full image metadata with official small and original image URLs.
- `spins`: full spin / 360-degree view metadata with official image URLs.
- `3dmodels`: full 3D model metadata with official GLB URLs.

## Added convenience columns

The `listings` table adds:

- `title`
- `brand`
- `color`
- `style`
- `product_type`
- `product_type_readable`
- `node_paths`
- `department`
- `hierarchy_path`
- `main_image_path`
- `main_image_url`

## License

Amazon Berkeley Objects is licensed under Creative Commons Attribution 4.0
International (CC BY 4.0).

Users must read and comply with the original ABO license and attribution
requirements before using the data.

Official dataset page:
https://amazon-berkeley-objects.s3.amazonaws.com/index.html

AWS Open Data Registry:
https://registry.opendata.aws/amazon-berkeley-objects/

## 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, Thomas Dideriksen, Kenan Deng, Himanshu Arora, Jasmine
Collins, and Jitendra Malik, with the complete author list in the ABO paper and
official documentation.