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Classification on Forest River South Bound (train)

85.8AccuracySimple CNN
75.81678.4088183.592Dec 23, 2025

Evaluation results

MethodLinks
Simple CNNLearning Rate=5e-42025.1285.80.76276.8
Simple CNNLearning Rate=1e-32025.1285.20.75576.2
Simple CNNLearning Rate=1e-42025.1284.80.75275.8
Simple CNNLearning Rate=5e-32025.12840.7475
Simple CNNLearning Rate=5e-52025.1283.80.74274.8
Simple CNNLearning Rate=1e-52025.1282.80.73273.8
Decision TreeLearning Rate=1e-52025.1282.50.67278.8
Decision TreeLearning Rate=5e-52025.1282.50.67278.8
Decision TreeLearning Rate=1e-42025.1282.50.67278.8
Decision TreeLearning Rate=5e-42025.1282.50.67278.8
Decision TreeLearning Rate=1e-32025.1282.50.67278.8
Decision TreeLearning Rate=5e-32025.1282.50.67278.8
Random ForestLearning Rate=1e-52025.12820.6773.2
Random ForestLearning Rate=5e-52025.12820.6773.2
Random ForestLearning Rate=1e-42025.12820.6773.2
Random ForestLearning Rate=5e-42025.12820.6773.2
Random ForestLearning Rate=1e-32025.12820.6773.2
Random ForestLearning Rate=5e-32025.12820.6773.2
SVM (RBF)Learning Rate=1e-52025.1281.80.62873
SVM (RBF)Learning Rate=5e-52025.1281.80.62873
SVM (RBF)Learning Rate=1e-42025.1281.80.62873
SVM (RBF)Learning Rate=5e-42025.1281.80.62873
SVM (RBF)Learning Rate=1e-32025.1281.80.62873
SVM (RBF)Learning Rate=5e-32025.1281.80.62873
Multi-Sensor Attention NetworkLearning Rate=5e-42025.1279.50.93775.9
Multi-Sensor Attention NetworkLearning Rate=1e-32025.1278.50.93374.8
Multi-Sensor Attention NetworkLearning Rate=1e-42025.12780.92874
Multi-Sensor Attention NetworkLearning Rate=5e-32025.1277.40.92573.5
Multi-Sensor Attention NetworkLearning Rate=5e-52025.1277.30.92373.3
Multi-Sensor Attention NetworkLearning Rate=1e-52025.1276.20.91672.2