| # Amazon Berkeley Objects (c) by Amazon.com |
|
|
| [Amazon Berkeley Objects](https://amazon-berkeley-objects.s3.us-east-1.amazonaws.com/index.html) |
| is a collection of product listings with multilingual metadata, catalog |
| imagery, high-quality 3d models with materials and parts, and benchmarks derived |
| from that data. |
|
|
| ## License |
|
|
| This work is licensed under the Creative Commons Attribution 4.0 International |
| Public License. To obtain a copy of the full license, see LICENSE-CC-BY-4.0.txt, |
| visit [CreativeCommons.org](https://creativecommons.org/licenses/by/4.0/) |
| or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. |
|
|
| 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. |
| |
| ## Attribution |
| |
| Credit for the data, including all images and 3d models, must be given to: |
|
|
| > Amazon.com |
|
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| 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) |
|
|
| ## Description |
|
|
| Amazon Berkeley Objects is a collection of 147,702 product listings with |
| multilingual metadata and 398,212 unique catalog images. 8,222 listings come |
| with turntable photography (also referred as *spin* or *360º-View* images), as |
| sequences of 24 or 72 images, for a total of 586,584 images in 8,209 unique |
| sequences. For 7,953 products, the collection also provides high-quality 3d |
| models, as glTF 2.0 files. |
|
|
| The collection is made of the following directories and files: |
|
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| * `README.md` - The present file. |
|
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| * `LICENSE-CC-BY-4.0.txt` - The License file. You must read, agree and comply |
| to the License before using the Amazon Berkeley Objects data. |
| |
| * `listings/` - Product description and metadata. Check `listings/README.md` |
| for details. `archives/abo-listings.tar` contains all the files in |
| `listings/` as a tar archive. |
| |
| * `images/` - Catalog imagery, in original and smaller (256px) resolution. |
| Check `images/README.md` for details. `archives/abo-images-original.tar` |
| contains the metadata and original images from `images/original/` as a tar |
| archive and `archives/abo-images-small.tar` contains the metadata and |
| downscaled images from `images/small/` as a tar archive. |
| |
| * `spins/` - Spin / 360º-View images and metadata. Check `spins/README.md` for |
| details. `archives/abo-spins.tar` contains the metadata and images from |
| `spins/` as a tar archive. |
| |
| * `3dmodels/` - 3D models and metadata. Check `3dmodels/README.md` for details. |
| `archives/abo-3dmodels.tar` contains the metadata and 3d models from |
| `3dmodels/` as a tar archive. |
| |
| * `benchmarks/abo-mvr.csv.xz` - Train/val/test dataset splits for the Multi- |
| View Retrieval experiments of the [CVPR 2022 ABO paper]( |
| https://amazon-berkeley-objects.s3.us-east-1.amazonaws.com/static_html/ABO_CVPR2022.pdf) |
| |
| * `archives/abo-benchmark-material.tar` - Train/test dataset for the Material |
| Prediction experiments of the [CVPR 2022 ABO paper]( |
| https://amazon-berkeley-objects.s3.us-east-1.amazonaws.com/static_html/ABO_CVPR2022.pdf). |
| See the `README.md` file in the archive for more details. |
| |
| * `archives/abo-part-labels.tar` - Dataset for the [2023 ABO Fine-grained |
| Semantic Segmentation Competition]( |
| https://eval.ai/web/challenges/challenge-page/2027/overview) |
| organized for the [3D Vision and Modeling Challenges in eCommerce Workshop]( |
| https://3dv-in-ecommerce.github.io) in conjunction with ICCV 2023. |
| |
| ## Footnotes |
|
|
| [^1]: Importantly, there is no guarantee that these URLs will remain unchanged |
| and available on the long term, we thus recommend using the images provided in |
| the archives instead. |
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|
