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 model 'karthiksv/vit-base-patch16-224-in21k-finetuned-cifar10'. The specific number of rows, columns, and file size is unknown.

Use Cases

• Analyze prediction confidence scores for the CIFAR-10 classification task to assess model calibration.
• Compare predicted labels against ground truth labels from the CIFAR-10 dataset to compute performance metrics.
• Benchmark the 'vit-base-patch16-224-in21k-finetuned-cifar10' model's outputs for multi-class image classification.

Strengths

• Predictions are for the well-known CIFAR-10 benchmark dataset.
• Generated by the AutoTrain platform, a standardized evaluation tool.
• Data is associated with a specific, named model architecture (ViT-base).

Limitations

• The dataset's size, row count, and column structure are unknown.
• Contains only model predictions, not the original CIFAR-10 images or labels.
• Data is from a single model evaluation, limiting comparative analysis.

Provenance

Source

Hugging Face AutoTrain Evaluator.

Collection Method

Model predictions generated automatically by the AutoTrain platform.

Freshness

Last updated on 2022-06-28.