Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
100K - 1M
License:
docs: fill in dataset card sections (bias, limitations, curation rationale)
#6
by samtuckervegan - opened
Commit history is no longer available for this pull request.
Fills in the dataset card sections that were previously marked as [More Information Needed].
Changes:
- Curation Rationale: describes why the 101 categories were chosen and the benchmark's design goals
- Source Data: documents that images came from Foodspotting.com and describes the data collection process
- Annotations: clarifies the difference between cleaned test labels and noisy training labels
- Personal and Sensitive Information: notes restaurant-setting photography context
- Social Impact: describes deployment contexts (dietary apps, nutrition tracking, recommendation systems) and global coverage limitations
- Discussion of Biases: documents category selection bias (Western restaurant cuisine skew), dietary category imbalance (~12-15% plant-based classes), label noise in training split, and photography style bias
- Other Known Limitations: covers noisy training labels, test set statistical power, missing nutritional metadata, and points to extended benchmarks with broader international coverage
- Dataset Curators: identifies ETH Zurich authors (Bossard, Guillaumin, Van Gool, ECCV 2014)
All information is sourced from the original paper and the dataset homepage.
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