VatsaDev/cnn_muffins

CNN Muffins

A compact dog-versus-muffin image-classification dataset built around the well-known visual confusion between Chihuahua faces and blueberry muffins.

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Dataset structure

The hard-16 benchmark is isolated from train and validation. The JSONL files use repository-relative image paths:

The benchmark labels follow the original 4x4 checkerboard layout and were corrected on September 2, 2026.

{"image":"train/dog/example.jpg","label":"dog","dedupe_sha256":"...","file_sha256":"..."}

Candidate filtering

Candidates were resized to 64×64 for comparison against the eight canonical tiles of the opposite class. A candidate was retained when at least one opposite-class reference satisfied all of the following:

• —MSE < 0.17
• —SSIM < 0.23
• —HSV histogram intersection ≥ 0.72
• —RGB mean/standard-deviation L2 distance ≤ 0.18

Retained candidates were deduplicated by SHA-256 before a deterministic, stratified 90/10 train-validation split.

Label-quality warning

The labels are provisional and intentionally not cleaned. A manual sample-by-sample visual audit of all 534 train/validation images found:

• —427 correct labels
• —101 confirmed label errors (18.9%)
• —6 ambiguous images
• —Dog-label confirmed error rate: 20.6%
• —Muffin-label confirmed error rate: 15.6%

Some generic cat-versus-dog sources were assigned labels from path heuristics, which introduced visible cat and out-of-class contamination. This release is therefore suitable for robustness experiments, noisy-label research, and pipeline debugging, but should not be treated as a clean supervised benchmark. The canonical hard-16 benchmark labels are separate and were not included in the 18.9% calculation.

Intended use

• —Training small CNN baselines without pretrained weights
• —Measuring robustness to noisy labels and hard visual negatives
• —Evaluating against the isolated canonical hard-16 benchmark

Provenance and licensing

Images were aggregated from multiple public dataset sources. Upstream licenses and usage terms may differ, and this repository does not grant additional rights. Users are responsible for checking source-specific requirements before redistribution or commercial use.