Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

ABO Furniture — Classroom Subset v1.0

Summary

A curated subset of the Amazon Berkeley Objects (ABO) dataset for use in the CAS Deep Learning — Computer Vision course exercises. Images show e-commerce product photographs of furniture across 6 categories, with multiple views per product item — making it well-suited for both classification and image retrieval exercises.

Source

Dataset: Amazon Berkeley Objects (ABO)
URL: https://amazon-berkeley-objects.s3.amazonaws.com/index.html
License: Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0)
Attribution: Collins et al., "ABO: Dataset and Benchmarks for Real-World 3D Object Understanding", CVPR 2022

Curation

Selected classes: bed, chair, lamp, sofa, storage, table
Mapping: product types mapped to 6 canonical labels via exact product_type matching
Views per item: primary image + up to 3 additional views (view_index 0–3)
Deduplication: global image_id deduplication — each image_id appears at most once
Split strategy: 80/10/10 stratified by item_id — all views of one item stay in the same split
Image processing: centre-cropped to 224×224, saved as JPEG quality 90
Format: ImageFolder layout (<split>/<label>/<image_id>.jpg)

Statistics

Class	Train	Val	Test	Total
bed	1,263	93	77	1,433
chair	8,112	961	905	9,978
lamp	3,499	411	388	4,298
sofa	2,966	333	322	3,621
storage	1,766	204	220	2,190
table	3,093	369	370	3,832
Total	20,699	2,371	2,282	25,352

Note: Chair is ~7× more frequent than bed. This reflects the original ABO distribution and is an intentional teaching point about class imbalance.

Sidecar files

metadata.csv — per-image record: image_id, item_id, label, split, is_primary, view_index, original_height, original_width, file_path
retrieval_groups.json — dict keyed by item_id; each value has label, split, and image_ids (list of all view image_ids for that item). Use this for image retrieval evaluation: query on one view, retrieve by item_id.

Usage in notebooks

from huggingface_hub import hf_hub_download
import tarfile, pathlib

archive = hf_hub_download(
    "marco-willi/abo_furniture",
    "abo_furniture.tar.gz",
    repo_type="dataset",
)
DATA_PATH = pathlib.Path("data")
with tarfile.open(archive) as tar:
    tar.extractall(DATA_PATH)
# Images are now at data/abo/<split>/<label>/<image_id>.jpg

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