Classification on Dog vs Cat
99.25AccuracyLSPIN
Evaluation results
| Method | Links | ||
|---|---|---|---|
| LSPIN2026.05 | 99.25 | — | |
| GBM2026.05 | 99.25 | — | |
| XGBoost2026.05 | 99.25 | — | |
| KNN2026.05 | 99.25 | — | |
| LSPINFeature extraction=ResNet50, Subsampling=11K2026.05 | 99.25 | — | |
| MLP2026.05 | 99.22 | — | |
| MLPFeature extraction=ResNet50, Subsampling=11K2026.05 | 99.22 | — | |
| DynaTab2026.05 | 99.2 | — | |
| DynaTabFeature extraction=ResNet50, Subsampling=11K2026.05 | 99.2 | — | |
| LLSPINFeature extraction=ResNet50, Subsampling=11K2026.05 | 99.19 | — | |
| Random Forest2026.05 | 99.15 | — | |
| LGBM2026.05 | 99.12 | — | |
| LGBMFeature extraction=ResNet50, Subsampling=11K2026.05 | 99.12 | — | |
| LLSPIN2026.05 | 99.11 | — | |
| STG2026.05 | 99.11 | — | |
| TabNet2026.05 | 99.09 | — | |
| TabNetFeature extraction=ResNet50, Subsampling=11K2026.05 | 99.09 | — | |
| SVM2026.05 | 99 | — | |
| CatBoost2026.05 | 98.98 | — | |
| Lasso2026.05 | 98.95 | — | |
| TabR2026.05 | 98.93 | — | |
| 1-D CNN2026.05 | 98.9 | — | |
| TabSeq2026.05 | 98.77 | — | |
| AdaBoost2026.05 | 98.69 | — | |
| TabM2026.05 | 98.52 | — | |
| CategoryEmbedding2026.05 | 98.45 | — | |
| Naive Bayes2026.05 | 97.88 | — | |
| Decision Tree2026.05 | 97.7 | — |