Classification on Forest River Median North Bound (train)
74.9AccuracyMulti-Sensor Attention Network
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Multi-Sensor Attention NetworkLearning Rate=5e-42025.12 | 74.9 | 0.937 | 71 | |
| Multi-Sensor Attention NetworkLearning Rate=1e-32025.12 | 74.2 | 0.93 | 69.5 | |
| Multi-Sensor Attention NetworkLearning Rate=1e-42025.12 | 73.5 | 0.924 | 68.6 | |
| Multi-Sensor Attention NetworkLearning Rate=5e-32025.12 | 72.7 | 0.92 | 68.1 | |
| Multi-Sensor Attention NetworkLearning Rate=5e-52025.12 | 72.5 | 0.917 | 67.8 | |
| Multi-Sensor Attention NetworkLearning Rate=1e-52025.12 | 71.4 | 0.908 | 66.7 | |
| Decision TreeLearning Rate=1e-52025.12 | 67.2 | 0.625 | 62.5 | |
| Simple CNNLearning Rate=5e-42025.12 | 65.8 | 0.718 | 56.5 | |
| Simple CNNLearning Rate=1e-32025.12 | 65.2 | 0.71 | 55.8 | |
| Simple CNNLearning Rate=1e-42025.12 | 65 | 0.708 | 55.5 | |
| Simple CNNLearning Rate=5e-52025.12 | 64.2 | 0.698 | 54.5 | |
| Simple CNNLearning Rate=5e-32025.12 | 64 | 0.695 | 54.8 | |
| Simple CNNLearning Rate=1e-52025.12 | 63.5 | 0.69 | 53.5 | |
| SVM (RBF)Learning Rate=1e-52025.12 | 62.5 | 0.612 | 48.8 | |
| Random ForestLearning Rate=1e-52025.12 | 62.5 | 0.642 | 53.8 |