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HyperAI超神经

Anomaly Detection On Fishyscapes 1

评估指标

AP

FPR95

评测结果

各个模型在此基准测试上的表现结果

			Paper Title	Repository
RPL+CoroCL	95.96	0.52	Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Mask2Anomaly	95.20	0.82	Unmasking Anomalies in Road-Scene Segmentation
PEBAL	92.38	1.73	Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
Synboost	72.59	18.75	Pixel-wise Anomaly Detection in Complex Driving Scenes
DenseHybrid	72.3	5.5	DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
FlowEneDet	67.8	21.58	Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow
SML	53.11	19.64	Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
Bayesian DeepLab	48.7	15.5	Evaluating Bayesian Deep Learning Methods for Semantic Segmentation

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Anomaly Detection On Fishyscapes 1 | SOTA | HyperAI超神经