--- 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/)