Classification on Forest River Median North Bound (test)
73.5AccuracyMulti-Sensor Attention Network
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Multi-Sensor Attention NetworkLearning Rate=5e-42025.12 | 73.5 | 0.925 | 69.5 | |
| Multi-Sensor Attention NetworkLearning Rate=1e-32025.12 | 72.8 | 0.918 | 68 | |
| Multi-Sensor Attention NetworkLearning Rate=1e-42025.12 | 72 | 0.912 | 67 | |
| Multi-Sensor Attention NetworkLearning Rate=5e-32025.12 | 71.2 | 0.908 | 66.5 | |
| Multi-Sensor Attention NetworkLearning Rate=5e-52025.12 | 71 | 0.905 | 66.2 | |
| Multi-Sensor Attention NetworkLearning Rate=1e-52025.12 | 69.8 | 0.895 | 65 | |
| Simple CNNLearning Rate=5e-42025.12 | 63.5 | 0.705 | 55.2 | |
| Simple CNNLearning Rate=1e-32025.12 | 62.8 | 0.698 | 54.5 | |
| Simple CNNLearning Rate=1e-42025.12 | 62.7 | 0.695 | 54 | |
| Simple CNNLearning Rate=5e-52025.12 | 61.8 | 0.685 | 52.8 | |
| Simple CNNLearning Rate=5e-32025.12 | 61.5 | 0.682 | 53.2 | |
| SVM (RBF)Learning Rate=1e-52025.12 | 61 | 0.597 | 47.1 | |
| Simple CNNLearning Rate=1e-52025.12 | 60.9 | 0.674 | 51.6 | |
| Random ForestLearning Rate=1e-52025.12 | 60.7 | 0.624 | 52.1 | |
| Decision TreeLearning Rate=1e-52025.12 | 52.7 | 0.55 | 51.3 |