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Scene labeling on CamVid 468/233 (test)

91.6Global Accuracy

VGG-conv5-DAG-RNN(8)

Updated 4d ago

Evaluation Results

Method	Links
VGG-conv5-DAG-RNN(8) 2015.09		91.6	78.1	-	-	-	-	-	-	-	-	-	-	-	-
VGG-conv5-ENN 2015.09		91	76.5	-	-	-	-	-	-	-	-	-	-	-	-
CNN-65-DAG-RNN(8) 2015.09		88.7	68.3	-	-	-	-	-	-	-	-	-	-	-	-
ReSeg 2015.11		88.7	68.1	86.8	84.7	93	87.3	48.6	98	63.3	20.9	35.6	87.3	43.5	58.8
SegNet 2015.11		88.6	65.9	88	87.3	92.3	80	29.5	97.6	57.2	49.4	27.8	84.8	30.7	50.2
CNN-65-DAG-RNN(4) 2015.09		88.2	66.3	-	-	-	-	-	-	-	-	-	-	-	-
Bayesian SegNet 2015.11		86.9	76.3	80.4	85.5	90.1	86.4	67.9	93.8	73.8	64.5	50.8	91.7	54.6	63.1
CNN-65 2015.09		84.3	53.2	-	-	-	-	-	-	-	-	-	-	-	-
SegNet-Basic 2015.11		84.3	62.9	75	84.6	91.2	82.7	36.9	93.3	55	37.5	44.8	74.1	16	-
CNN-65-ENN 2015.09		84.1	58.1	-	-	-	-	-	-	-	-	-	-	-	-
Tighe et al. 2015.09		83.9	62.5	-	-	-	-	-	-	-	-	-	-	-	-
Sturgess et al. 2015.09		83.8	59.2	-	-	-	-	-	-	-	-	-	-	-	-
Ladicky et al. 2015.09		83.8	62.5	-	-	-	-	-	-	-	-	-	-	-	-
Boosting+Higher order 2015.11		83.8	59.2	84.5	72.6	97.5	72.7	34.1	95.3	34.2	45.7	8.1	77.6	28.5	-
Boosting+Detectors+CRF 2015.11		83.8	62.5	81.5	76.6	96.2	78.7	40.2	93.9	43	47.6	14.3	81.5	33.9	-
ReSeg 2015.11		83.5	73.2	70.6	84.6	89.6	81.1	61	95.1	80.4	35.6	60.6	86.3	60	53.7
Supa Parsing 2015.11		83.3	51.2	87	67.1	96.9	62.7	30.1	95.9	14.7	17.9	1.7	70	19.4	-
SegNet-Basic 2015.11		82.8	62.3	80.6	72	93	78.5	21	94	62.5	31.4	36.6	74	42.5	46.3
Zhang et al. 2015.09		82.1	55.4	-	-	-	-	-	-	-	-	-	-	-	-
Bulo et al. 2015.09		82.1	56.1	-	-	-	-	-	-	-	-	-	-	-	-
Bayesian SegNet-Basic 2015.11		81.6	70.5	75.1	68.8	91.4	77.7	52	92.5	71.5	44.9	52.9	79.1	69.6	55.8
Tighe et al. 2015.09		78.6	43.8	-	-	-	-	-	-	-	-	-	-	-	-