Model Predictions for Multi-Class Image Classification on CIFAR-10

Model predictions generated by AutoTrain for a multi-class image classification task using the michaelbenayoun/vit-base-beans model on the CIFAR-10 dataset. The dataset was last updated on June 28, 2022. The specific number of rows, columns, and file size are unknown.

Use Cases

• Compare model predictions against CIFAR-10 ground truth labels to assess classification performance.
• Analyze prediction patterns from the michaelbenayoun/vit-base-beans model for potential error modes.
• Use the generated predictions as a benchmark for evaluating other image classification models on CIFAR-10.

Strengths

• Predictions are generated by a specific, named model (michaelbenayoun/vit-base-beans) for a well-known benchmark dataset (CIFAR-10).
• The dataset is associated with a defined task (Multi-class Image Classification).
• The dataset was last updated on a specific date: June 28, 2022.

Limitations

• The dataset's core structure is unknown, including the number of rows, columns, and specific prediction format.
• The data is over two years old and may not reflect the current performance of the evaluated model.
• Without access to the sample data or column definitions, the usability and interpretability of the predictions are limited.

Provenance

Source

AutoTrain Evaluator on Hugging Face.

Collection Method

Model predictions generated automatically by the AutoTrain platform.

Freshness

Last updated on 2022-06-28.