Image Classification on SAT-6 (test)
99.84AccuracyDeepSat V2
Evaluation Results
| Method | Links | |
|---|---|---|
| DeepSat V22019.11 | 99.84 | |
| Triplet networks2019.11 | 99.71 | |
| SatCNNNormalization=Z-score2019.11 | 99.61 | |
| SatCNNNormalization=linear2019.11 | 99.58 | |
| D-DSML-CaffeNet2019.11 | 99.42 | |
| Contrastive loss2019.11 | 98.55 | |
| TradCNNNormalization=Z-score2019.11 | 98.34 | |
| MLPNormalization=Z-score2019.11 | 97.46 | |
| DCNN2019.11 | 96.04 | |
| DeepSat2019.11 | 93.92 | |
| DeepSatNeurons per layer=50, Layers=22015.09 | 93.916 | |
| DeepSatNeurons per layer=20, Layers=32015.09 | 93.42 | |
| DeepSatNeurons per layer=50, Layers=32015.09 | 92.65 | |
| DeepSatNeurons per layer=10, Layers=22015.09 | 91.91 | |
| DeepSatNeurons per layer=100, Layers=32015.09 | 91.057 | |
| DeepSatNeurons per layer=100, Layers=22015.09 | 89.08 | |
| DeepSatNeurons per layer=10, Layers=32015.09 | 87.716 | |
| DeepSatNeurons per layer=20, Layers=22015.09 | 86.21 | |
| CNN2019.11 | 79.1 | |
| SDAE2019.11 | 78.43 | |
| DBN2019.11 | 76.47 |