Classification on Food-MNIST 10 classes (val)
73.84AccuracyCNN (2 Conv Layers)
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| CNN (2 Conv Layers)Optimizer=Adam, Learning rate=0.001, Batch size=32, Epochs=102025.07 | 73.84 | 75.21 | 73.84 | 74.41 | |
| Random ForestImplementation=scikit-learn, Hyperparameters=standard2025.07 | 68.92 | 71.03 | 68.92 | 69.85 | |
| Fully Connected (2-layer)Optimizer=Adam, Learning rate=0.001, Batch size=32, Epochs=102025.07 | 67.45 | 68.34 | 67.45 | 67.82 | |
| Gradient BoostingImplementation=scikit-learn, Hyperparameters=standard2025.07 | 64.73 | 66.12 | 64.73 | 65.35 | |
| K-Nearest NeighborsImplementation=scikit-learn, Hyperparameters=standard2025.07 | 62.18 | 63.41 | 62.18 | 62.73 | |
| AdaBoostImplementation=scikit-learn, Hyperparameters=standard2025.07 | 58.35 | 59.67 | 58.35 | 58.94 | |
| Decision TreeImplementation=scikit-learn, Hyperparameters=standard2025.07 | 56.92 | 58.34 | 56.92 | 57.51 | |
| MLP (Shallow NN)Optimizer=Adam, Learning rate=0.001, Batch size=32, Epochs=102025.07 | 54.67 | 56.21 | 54.67 | 55.35 | |
| SVMImplementation=scikit-learn, Hyperparameters=standard2025.07 | 51.23 | 52.87 | 51.23 | 51.96 | |
| Logistic RegressionImplementation=scikit-learn, Hyperparameters=standard2025.07 | 49.86 | 51.42 | 49.86 | 50.56 | |
| Gaussian NBImplementation=scikit-learn, Hyperparameters=standard2025.07 | 42.35 | 44.87 | 42.35 | 43.47 |