AutoTrain Model Predictions for Food101 Image Classification

Featuring model predictions generated by AutoTrain for a multi-class image classification task. The evaluated model is eslamxm/vit-base-food101, and the source dataset is Food101. The number of rows, columns, and specific data fields are unknown.

Use Cases

• Compare model predictions against ground truth labels for the Food101 dataset to assess classification accuracy.
• Analyze prediction patterns from the eslamxm/vit-base-food101 model to identify challenging food categories.
• Use the AutoTrain-generated predictions as a benchmark for evaluating other image classification models on the Food101 task.

Strengths

• Predictions are from a specific, named model (eslamxm/vit-base-food101) evaluated on a known benchmark dataset (Food101).
• The data is associated with tags indicating a size category of 10K<n<100K, suggesting a moderate scale.

Limitations

• The specific columns, sample data, and total row count are unknown, limiting immediate analytical utility.
• The data is from a single model evaluation run, providing a narrow view of model performance.

Provenance

Source

Hugging Face AutoTrain Evaluator (autoevaluate).

Collection Method

Model predictions generated automatically by the AutoTrain platform for evaluation.

Freshness

Last updated on 2022-06-28.

Geography

Region tag indicates 'us', but spatial coverage of the underlying Food101 dataset is unspecified.