AutoTrain Model Predictions for CIFAR-10 Image Classification

Aggregating model predictions generated by AutoTrain for a multi-class image classification task on the CIFAR-10 dataset. The predictions were produced by the 'aaraki/vit-base-patch16-224-in21k-finetuned-cifar10' model. The number of rows, columns, and specific data fields are unknown.

Use Cases

• Compare model predictions against CIFAR-10 ground truth labels to evaluate classification accuracy.
• Analyze prediction confidence scores, if present in the data, to identify challenging image categories.
• Use the generated predictions as a benchmark for testing new evaluation metrics on image classification tasks.

Strengths

• Predictions are from a specific, named model (aaraki/vit-base-patch16-224-in21k-finetuned-cifar10).
• Task and source dataset (CIFAR-10 for multi-class image classification) are clearly defined.
• Data is associated with the AutoTrain platform, a known tool for automated model evaluation.

Limitations

• The dataset's internal structure, including column names and row count, is completely unknown.
• The data is over two years old (last updated June 2022), which may limit relevance for benchmarking newer models.
• Without sample data or a schema, the usability and required preprocessing steps are unclear.

Provenance

Source

AutoTrain (autoevaluate) on Hugging Face

Collection Method

Model predictions generated by the AutoTrain automatic evaluator.

Freshness

2022-06-28