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  ### Dataset Summary
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- [Data-centric AI](https://datacentricai.org) principles have become increasingly important for real-world use cases. At [Renumics](https://renumics.com/?hf-dataset-card=food101-enriched) we believe that classical benchmark datasets and competitions should be extended to reflect this development.
 
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- This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the following ways:
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  1. Enable new researchers to quickly develop a profound understanding of the dataset.
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  2. Popularize data-centric AI principles and tooling in the ML community.
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  3. Encourage the sharing of meaningful qualitative insights in addition to traditional quantitative metrics.
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- This dataset is an enriched version of the [Food101 Data Set](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/).
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  ### Explore the Dataset
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  ### Dataset Summary
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+ ๐Ÿ“Š [Data-centric AI](https://datacentricai.org) principles have become increasingly important for real-world use cases.
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+ At [Renumics](https://renumics.com/?hf-dataset-card=food101-enriched) we believe that classical benchmark datasets and competitions should be extended to reflect this development.
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+ ๐Ÿ” This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the following ways:
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  1. Enable new researchers to quickly develop a profound understanding of the dataset.
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  2. Popularize data-centric AI principles and tooling in the ML community.
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  3. Encourage the sharing of meaningful qualitative insights in addition to traditional quantitative metrics.
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+ ๐Ÿ“š This dataset is an enriched version of the [Food101 Data Set](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/).
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  ### Explore the Dataset
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