Model Predictions for CIFAR-10 Image Classification

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 are not provided.

Use Cases

• Evaluate the performance of the tanlq/vit-base-patch16-224-in21k-finetuned-cifar10 model on CIFAR-10 classification tasks.
• Compare model predictions against ground truth labels from the CIFAR-10 dataset for benchmarking.
• Analyze prediction patterns for 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 for multi-class image classification) are clearly defined.
• Data is associated with the AutoTrain platform, a known model evaluation tool.

Limitations

• The dataset's structure, including column names and row count, is unknown.
• The data is from 2022 and may not reflect the performance of more recent model versions.
• Without the original CIFAR-10 test data, the predictions cannot be directly validated.

Provenance

Source

Hugging Face AutoTrain Evaluator (autoevaluate).

Collection Method

Model predictions generated automatically by the AutoTrain platform.

Freshness

Last updated on 2022-06-28.