AutoTrain Model Predictions for CIFAR-10 Image Classification

Featuring model predictions generated by AutoTrain for a multi-class image classification task on the CIFAR-10 dataset. The predictions were produced by the tanlq/vit-base-patch16-224-in21k-finetuned-cifar10 model. The specific number of rows, columns, and file size is unknown.

Use Cases

• Analyze prediction confidence scores across the 10 CIFAR-10 classes to assess model calibration.
• Compare predicted labels against ground truth labels to compute task-specific evaluation metrics.
• Use the model's output logits for error analysis on specific image categories within the CIFAR-10 dataset.

Strengths

• Predictions are from a specific, named model (tanlq/vit-base-patch16-224-in21k-finetuned-cifar10).
• Task and source dataset (CIFAR-10) are explicitly defined for context.
• Data is associated with the AutoTrain platform, a known tool for model evaluation.

Limitations

• The dataset's core structure is unknown, as column names and sample data are unavailable.
• The total number of prediction records (rows) is not specified, preventing assessment of scale.
• The data is from a single model evaluation, limiting comparative analysis.

Provenance

Source

autoevaluate on Hugging Face

Collection Method

Model predictions generated by the Hugging Face AutoTrain evaluator.

Freshness

2022-06-29