Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures

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Benchmark Model Rank Results
image-classification-on-mnistSimpleNetv1#10Percentage error: 0.25
image-classification-on-cifar-10SimpleNetv1#158Percentage correct: 95.51
image-classification-on-cifar-100SimpleNetv1#169Percentage correct: 78.37
image-classification-on-imagenetSimpleNetV1-9m-correct-labels#692Top 1 Accuracy: 81.24Number of params: 9.5M
image-classification-on-imagenetSimpleNetV1-5m-correct-labels#811Top 1 Accuracy: 79.12Number of params: 5.7M
image-classification-on-imagenetSimpleNetV1-small-075-correct-labels#980Top 1 Accuracy: 75.66Number of params: 3M
image-classification-on-imagenetSimpleNetV1-9m#1021Top 1 Accuracy: 74.17Number of params: 9.5M
image-classification-on-imagenetSimpleNetV1-5m#1055Top 1 Accuracy: 71.94Number of params: 5.7M
image-classification-on-imagenetSimpleNetV1-small-05-correct-labels#1083Top 1 Accuracy: 69.11Number of params: 1.5M
image-classification-on-imagenetSimpleNetV1-small-075#1087Top 1 Accuracy: 68.15Number of params: 3M
image-classification-on-imagenetSimpleNetV1-small-05#1102Top 1 Accuracy: 61.52Number of params: 1.5M

Rank counts only results with a code link.