Add Amazon Berkeley Objects metadata mirror

ORIGINAL_LICENSE-CC-BY-4.0.txt

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ORIGINAL_README.md

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+# 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
+
+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:
+
+  * `README.md` - The present file.
+
+  * `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.

README.md

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+---
+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.

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