Ablation Study Results for a Computer Vision Architecture

Ablation study results for a proposed neural architecture, comparing components like DenseNet121 and Swin Transformer V2. The dataset contains accuracy gains and p-values from paired t-tests across N=10 measurements from two runs of 5-fold cross-validation. It was authored by Max Wiedemann and last updated on 2026-05-05.

Use Cases

• Compare the performance impact of DenseNet121 versus Swin Transformer V2 components based on the described ablation study.
• Evaluate the statistical significance of architectural changes using the provided p-values from paired t-tests.
• Analyze the accuracy gain in percentage points for different model configurations relative to a full architecture.
• Replicate or validate the ablation study methodology involving two independent runs of 5-fold cross-validation.

Strengths

• Statistical results are derived from N=10 measurements, providing a basis for paired t-tests.
• The study design includes two independent runs of 5-fold cross-validation, which suggests a structured evaluation approach.
• Specific architectural components like DenseNet121, Swin Transformer V2, and deformable convolution layers are clearly defined for comparison.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale analysis.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope and likely a summary of results rather than raw data.

Provenance

Source

figshare

Collection Method

Results from an ablation study of a neural architecture, likely from a research experiment.

Freshness

Last updated 2026-05-05 17:31:05; freshness should be verified.