SlowGuess/NeurIPS_2023_OCR huggingface dataset:
SegFormer-MiTB2 min: 4.64, max: 99.75 mean: 58.27, std: 15.97 skew: 0.09, kurtosis: -0.16
DeepLabV3-ResNet101 min: 11.16, max: 99.68 mean: 57.27, std: 16.14 skew: 0.10, kurtosis: -0.12
PSPNet-ResNet101 min: 11.51, max: 99.63 mean: 57.37, std: 15.87 skew: 0.12, kurtosis: -0.14
UNet-ResNet101 min: 1.31, max: 99.70 mean: 56.86, std: 16.17 skew: 0.09, kurtosis: -0.17
Figure 26: Histograms: ADE20K.
DeepLabV3+-ResNet101: ADE_val_00000626 (IoU<sub>i</sub> = 5.99).
DeepLabV3+-ResNet50: ADE_val_00000223 (IoU<sub>i</sub> = 6.91).
DeepLabV3+-ResNet34: ADE_val_00001600 (IoU $_i^{\mathrm{I}} = 0.00$ ).
DeepLabV3+-ResNet18: ADE_val_00000263 (IoU<sub>i</sub> = 0.01).
Figure 27: Worst-case images: ADE20K 1/3. Left: image. Middle: label. Right: prediction.
DeepLabV3+-EfficientNetB0: ADE_val_00000263 (IoU<sub>i</sub> = 0.00).
DeepLabV3+-MobileNetV2: ADE_val_00001600 (IoU i = 0.00).
DeepLabV3+-MobileViTV2: ADE_val_00000054 (IoU<sub>i</sub> = 4.27).
UPerNet-ResNet101: ADE_val_00001229 (IoU<sub>i</sub> = 0.06).
UPerNet-ConvNeXt: ADE_val_00001897 (IoU<sub>i</sub> = 0.00).
Figure 28: Worst-case images: ADE20K 2 / 3. Left: image. Middle: label. Right: prediction.
UPerNet-MiTB4: ADE_val_00001951 (IoU $_i^{\mathrm{I}} = 0.58$ ).
SegFormer-MiTB4: ADE_val_00001600 (IoU<sub>i</sub> = 1.14).
SegFormer-MiTB2: ADE_val_00000054 (IoU $_i^{\mathrm{I}} = 2.21$ ).
DeepLabV3-ResNet101: ADE_val_00000626 (IoU<sub>i</sub> = 6.21).
PSPNet-ResNet101: ADE_val_00001596 (IoU<sub>i</sub> = 4.81).
Figure 29: Worst-case images: ADE20K 3 / 3. Left: image. Middle: label. Right: prediction.
UNet-ResNet101: ADE_val_00000263 (IoU<sub>i</sub> = 0.00).
G.7 COCO-Stuff
Table 20: Results: COCO-Stuff. Red: the best for each metric. Green: the worst for each metric.
Method	Backbone	mIoU1	mIoUq	mIoU5	mIoU1
mIoUC	mIoUCq	mIoUC5	mIoUC1
mIoUD	mAcc	Acc
DeepLabV3+	ResNet101	48.71 ± 0.06	35.26 ± 0.09	17.07 ± 0.22	8.48 ± 0.39
41.98 ± 0.04	22.46 ± 0.04	1.33 ± 0.07	0.00 ± 0.00
44.09 ± 0.17	59.35 ± 0.14	69.56 ± 0.12
DeepLabV3+	ResNet50	46.91 ± 0.04	33.39 ± 0.06	15.40 ± 0.23	7.34 ± 0.53
39.66 ± 0.04	20.75 ± 0.08	1.19 ± 0.04	0.00 ± 0.00
42.15 ± 0.03	56.93 ± 0.08	68.63 ± 0.01
DeepLabV3+	ResNet34	43.32 ± 0.04	29.54 ± 0.08	11.76 ± 0.20	3.76 ± 0.23
34.39 ± 0.06	16.77 ± 0.03	0.56 ± 0.04	0.00 ± 0.00
36.30 ± 0.18	50.50 ± 0.16	66.12 ± 0.07
DeepLabV3+	ResNet18	41.60 ± 0.05	27.86 ± 0.07	10.48 ± 0.15	2.79 ± 0.27
32.33 ± 0.04	15.43 ± 0.06	0.38 ± 0.03	0.00 ± 0.00
34.18 ± 0.18	47.90 ± 0.17	64.78 ± 0.05
DeepLabV3+	EfficientNetB0	42.61 ± 0.16	28.84 ± 0.25	11.39 ± 0.54	3.92 ± 0.44
33.64 ± 0.24	16.22 ± 0.19	0.35 ± 0.08	0.00 ± 0.00
35.65 ± 0.22	50.23 ± 0.32	65.59 ± 0.16
DeepLabV3+	MobileNetV2	40.36 ± 0.09	26.64 ± 0.08	9.54 ± 0.03	2.57 ± 0.12
30.95 ± 0.05	14.38 ± 0.04	0.36 ± 0.03	0.00 ± 0.00
32.65 ± 0.17	46.15 ± 0.09	63.87 ± 0.08
DeepLabV3+	MobileViTV2	44.54 ± 0.02	30.90 ± 0.02	13.68 ± 0.11	6.35 ± 0.29
36.23 ± 0.03	18.06 ± 0.06	0.75 ± 0.10	0.00 ± 0.00
38.99 ± 0.03	53.15 ± 0.12	67.01 ± 0.03
UPerNet	ResNet101	48.16 ± 0.10	34.73 ± 0.13	16.69 ± 0.24	8.20 ± 0.76
41.16 ± 0.16	21.75 ± 0.14	1.31 ± 0.05	0.00 ± 0.00
43.87 ± 0.07	58.57 ± 0.08	69.47 ± 0.03
UPerNet	ConvNeXt	51.34 ± 0.04	38.06 ± 0.02	19.77 ± 0.05	10.99 ± 0.07
44.91 ± 0.04	24.74 ± 0.03	1.83 ± 0.04	0.00 ± 0.00
48.51 ± 0.03	62.61 ± 0.06	71.78 ± 0.04
UPerNet	MiTB4	50.74 ± 0.03	37.23 ± 0.05	19.11 ± 0.20	11.50 ± 0.45
43.69 ± 0.07	23.62 ± 0.05	1.52 ± 0.10	0.00 ± 0.00
47.02 ± 0.14	61.41 ± 0.15	71.52 ± 0.04
SegFormer	MiTB4	50.46 ± 0.17	37.02 ± 0.17	19.02 ± 0.03	11.52 ± 0.13
43.33 ± 0.24	23.37 ± 0.20	1.47 ± 0.08	0.00 ± 0.00
46.73 ± 0.37	61.32 ± 0.19	71.34 ± 0.11
SegFormer	MiTB2	48.74 ± 0.02	35.28 ± 0.01	17.65 ± 0.06	10.12 ± 0.37
41.36 ± 0.00	21.89 ± 0.04	1.46 ± 0.09	0.00 ± 0.00
44.99 ± 0.09	59.44 ± 0.04	70.35 ± 0.02
DeepLabV3	ResNet101	48.21 ± 0.01	34.91 ± 0.02	17.12 ± 0.09	9.12 ± 0.08
41.37 ± 0.11	21.96 ± 0.07	1.34 ± 0.01	0.00 ± 0.00
44.29 ± 0.03	59.29 ± 0.14	69.63 ± 0.09
PSPNet	ResNet101	48.05 ± 0.08	34.78 ± 0.08	16.90 ± 0.08	8.49 ± 0.53
41.30 ± 0.04	21.94 ± 0.07	1.30 ± 0.07	0.00 ± 0.00
44.19 ± 0.05	59.04 ± 0.01	69.51 ± 0.14
UNet	ResNet101	45.80 ± 0.10	32.54 ± 0.06	14.73 ± 0.33	7.27 ± 0.91
37.76 ± 0.19	19.43 ± 0.26	0.97 ± 0.20	0.00 ± 0.00
37.14 ± 0.34	51.43 ± 0.25	64.68 ± 0.14
DeepLabV3+-ResNet101  
min: 0.00, max: 99.89  
mean: 48.71, std: 18.06  
skew: 0.50, kurtosis: -0.07
DeepLabV3+-ResNet50 min: 0.00, max: 99.94 mean: 46.91, std: 18.26 skew: 0.54, kurtosis: -0.03
DeepLabV3+-ResNet34  
min: 0.00, max: 99.51  
mean: 43.31, std: 18.72  
skew: 0.63, kurtosis: 0.08
DeepLabV3+-ResNet18 min: 0.00, max: 99.27 mean: 41.60, std: 18.82 skew: 0.68, kurtosis: 0.15
DeepLabV3+-EfficientNetB0 min: 0.00, max: 99.87 mean: 42.60, std: 18.67 skew: 0.67, kurtosis: 0.09
DeepLabV3+-MobileNetV2 min:0.00,max:99.11 mean:40.36,std:18.87 skew:0.71,kurtosis:0.21
DeepLabV3+-MobileViTV2 min:0.00,max:99.78 mean:44.54,std:18.63 skew:0.64,kurtosis:0.05
UPerNet-ResNet101 min: 0.00, max: 99.97 mean: 48.16, std: 18.07 skew: 0.52, kurtosis: -0.06
UPerNet-ConvNeXt min: 0.03, max: 99.94 mean: 51.34, std: 17.72 skew: 0.43, kurtosis: -0.15
UPerNet-MiTB4 min: 0.00, max: 99.96 mean: 50.74, std: 18.02 skew: 0.46, kurtosis: -0.20
SegFormer-MiTB4 min: 0.00, max: 99.99 mean: 50.46, std: 17.94 skew: 0.46, kurtosis: -0.19
SegFormer-MiTB2 min: 0.00, max: 99.76 mean: 48.75, std: 18.16 skew: 0.52, kurtosis: -0.11
DeepLabV3-ResNet101 min: 0.00, max: 99.94 mean: 48.21, std: 17.91 skew: 0.52, kurtosis: -0.07
PSPNet-ResNet101 min: 0.00, max: 99.98 mean: 48.05, std: 17.88 skew: 0.53, kurtosis: -0.05
UNet-ResNet101 min: 0.00, max: 99.05 mean: 45.80, std: 17.90 skew: 0.56, kurtosis: 0.02
Figure 30: Histograms: COCO-Stuff.
DeepLabV3+-ResNet101: 000000064574 (IoU<sub>i</sub> = 0.00).
DeepLabV3+-ResNet50: 000000064574 (IoU<sub>i</sub> = 0.00).
DeepLabV3+-ResNet34: 000000027932  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
DeepLabV3+-ResNet18: 000000064574 (IoU<sub>i</sub> = 0.00).
Figure 31: Worst-case images: COCO-Stuff 1 / 3. Left: image. Middle: label. Right: prediction.
DeepLabV3+-EfficientNetB0: 000000064574  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
DeepLabV3+-MobileNetV2: 000000027932 (IoU<sub>i</sub> = 0.00).
DeepLabV3+-MobileViTV2: 000000064574  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
UPerNet-ResNet101: 000000064574 (IoU $_i^{\mathrm{I}} = 0.00$ ).
UPerNet-ConvNeXt: 000000064574 (IoU<sub>i</sub> = 0.00).
Figure 32: Worst-case images: COCO-Stuff 2 / 3. Left: image. Middle: label. Right: prediction.
UPerNet-MiTB4: 000000064574 (IoU $_i^{\mathrm{I}} = 0.00$ ).
SegFormer-MiTB4: 000000064574 (IoU<sub>i</sub> = 0.00).
SegFormer-MiTB2: 000000064574  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
DeepLabV3-ResNet101: 000000053529 (IoU<sub>i</sub> = 0.00).
PSPNet-ResNet101: 000000053529  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
UNet-ResNet101: 000000064574 (IoU $_i^{\mathrm{I}} = 0.00$ ).
Figure 33: Worst-case images: COCO-Stuff 3 / 3. Left: image. Middle: label. Right: prediction.
G.8 PASCAL VOC
Table 21: Results: PASCAL VOC. Red: the best for each metric. Green: the worst for each metric.
Method	Backbone	mIoU1	mIoUq	mIoU5	mIoU1
mIoUC	mIoUCq	mIoUC5	mIoUC1
mIoUD	mAcc	Acc
DeepLabV3+	ResNet101	86.95 ± 0.06	75.76 ± 0.09	45.67 ± 0.27	31.61 ± 0.41
79.07 ± 0.18	62.71 ± 0.39	17.32 ± 1.27	1.67 ± 0.47
81.07 ± 0.28	88.68 ± 0.34	95.27 ± 0.02
DeepLabV3+	ResNet50	85.80 ± 0.12	74.12 ± 0.17	45.54 ± 0.41	30.95 ± 0.42
77.56 ± 0.03	60.20 ± 0.21	13.03 ± 1.65	2.67 ± 1.25
79.71 ± 0.14	87.66 ± 0.22	94.86 ± 0.11
DeepLabV3+	ResNet34	82.96 ± 0.09	69.47 ± 0.24	39.79 ± 0.54	24.23 ± 0.82
73.16 ± 0.19	52.11 ± 0.38	5.12 ± 0.32	0.33 ± 0.47
74.37 ± 0.68	84.97 ± 0.53	93.54 ± 0.11
DeepLabV3+	ResNet18	81.95 ± 0.07	68.27 ± 0.18	38.89 ± 0.37	24.60 ± 1.02
71.63 ± 0.17	50.42 ± 0.22	4.04 ± 0.21	0.00 ± 0.00
73.30 ± 0.22	83.86 ± 0.23	93.16 ± 0.04
DeepLabV3+	EfficientNetB0	82.22 ± 0.06	68.57 ± 0.10	38.06 ± 0.14	21.28 ± 0.63
72.06 ± 0.13	51.56 ± 0.14	5.73 ± 0.51	0.00 ± 0.00
73.58 ± 0.78	83.85 ± 0.47	93.32 ± 0.16
DeepLabV3+	MobileNetV2	81.00 ± 0.09	66.93 ± 0.14	37.47 ± 0.18	22.27 ± 0.36
70.06 ± 0.10	48.38 ± 0.25	3.37 ± 0.44	0.67 ± 0.47
72.05 ± 0.24	82.75 ± 0.41	92.89 ± 0.05
DeepLabV3+	MobileViTV2	83.41 ± 0.12	70.12 ± 0.20	39.78 ± 0.68	26.77 ± 1.44
73.32 ± 0.17	53.02 ± 0.43	5.91 ± 0.76	0.00 ± 0.00
75.75 ± 0.20	85.34 ± 0.18	93.95 ± 0.07
UPerNet	ResNet101	86.52 ± 0.03	74.98 ± 0.08	44.76 ± 0.25	29.14 ± 0.53
78.23 ± 0.08	61.48 ± 0.26	14.80 ± 0.90	1.67 ± 1.25
80.50 ± 0.15	87.70 ± 0.07	95.19 ± 0.04
UPerNet	ConvNeXt	88.30 ± 0.11	78.14 ± 0.27	49.92 ± 0.64	33.31 ± 0.64
81.08 ± 0.25	66.35 ± 0.39	24.58 ± 0.52	8.33 ± 1.25
83.48 ± 0.08	89.01 ± 0.17	95.95 ± 0.03
UPerNet	MiTB4	87.76 ± 0.12	76.98 ± 0.27	47.25 ± 0.72	32.96 ± 0.19
80.02 ± 0.29	63.68 ± 0.54	16.30 ± 2.20	3.00 ± 1.41
82.71 ± 0.22	89.58 ± 0.30	95.73 ± 0.05
SegFormer	MiTB4	87.54 ± 0.15	76.74 ± 0.28	46.88 ± 0.32	30.82 ± 0.82
79.80 ± 0.30	63.89 ± 0.51	20.05 ± 1.57	4.67 ± 1.70
82.33 ± 0.35	89.16 ± 0.29	95.65 ± 0.08
SegFormer	MiTB2	86.26 ± 0.10	74.51 ± 0.18	43.45 ± 0.31	28.08 ± 0.68
77.57 ± 0.23	60.23 ± 0.15	11.76 ± 0.50	1.33 ± 0.47
80.45 ± 0.29	88.02 ± 0.30	95.17 ± 0.08
DeepLabV3	ResNet101	86.32 ± 0.03	75.01 ± 0.08	45.09 ± 0.24	31.52 ± 1.30
77.93 ± 0.09	61.43 ± 0.21	17.67 ± 0.67	5.33 ± 0.94
81.24 ± 0.09	88.27 ± 0.23	95.30 ± 0.01
PSPNet	ResNet101	86.23 ± 0.05	74.85 ± 0.14	45.39 ± 0.74	31.36 ± 1.48
77.84 ± 0.11	61.11 ± 0.28	15.95 ± 1.32	3.33 ± 1.70
81.10 ± 0.16	88.13 ± 0.23	95.25 ± 0.05
UNet	ResNet101	85.58 ± 0.09	73.74 ± 0.16	43.90 ± 0.82	29.84 ± 1.25
76.85 ± 0.10	59.49 ± 0.24	16.00 ± 1.34	3.33 ± 1.25
74.94 ± 0.33	83.62 ± 0.20	94.06 ± 0.09
DeepLabV3+-ResNet101  
min: 16.20, max: 99.93  
mean: 86.95, std: 13.44  
skew: -1.72, kurtosis: 2.70
DeepLabV3+-ResNet50  
min: 12.35, max: 99.96  
mean: 85.79, std: 13.97  
skew: -1.54, kurtosis: 2.00
DeepLabV3+-ResNet34  
min: 11.71, max: 99.85  
mean: 82.96, std: 15.73  
skew: -1.32, kurtosis: 1.25
DeepLabV3+-ResNet18  
min: 9.81, max: 99.84  
mean: 81.95, std: 15.98  
skew: -1.24, kurtosis: 1.06
DeepLabV3+-EfficientNetB0 min: 0.64, max: 99.80 mean: 82.22, std: 15.72 skew: -1.33, kurtosis: 1.69
DeepLabV3+-MobileNetV2 min: 9.76, max: 99.62 mean: 81.00, std: 16.48 skew: -1.16, kurtosis: 0.80
DeepLabV3+-MobileViTV2 min:11.81,max:99.92 mean:83.41,std:15.61 skew:-1.35,kurtosis:1.24
UPerNet-ResNet101 min: 11.58, max: 99.95 mean: 86.52, std: 13.86 skew: -1.74, kurtosis: 3.10
UPerNet-ConvNeXt min: 12.15, max: 99.89 mean: 88.30, std: 12.48 skew: -1.96, kurtosis: 4.20
UPerNet-MiTB4 min:24.94,max:99.97 mean:87.76,std:12.97 skew:-1.80,kurtosis:2.98
SegFormer-MiTB4 min: 8.19, max: 99.97 mean: 87.54, std: 13.06 skew: -1.85, kurtosis: 3.54
SegFormer-MiTB2 min: 9.36, max: 99.94 mean: 86.26, std: 14.29 skew: -1.75, kurtosis: 2.94
DeepLabV3-ResNet101 min: 13.19, max: 99.68 mean: 86.32, std: 13.71 skew: -1.71, kurtosis: 2.77
PSPNet-ResNet101 min: 17.11, max: 99.70 mean: 86.23, std: 13.76 skew: -1.67, kurtosis: 2.56
UNet-ResNet101 min: 7.14, max: 99.93 mean: 85.58, std: 14.16 skew: -1.59, kurtosis: 2.41
Figure 34: Histograms: PASCAL VOC.
DeepLabV3+-ResNet101: 2010_001327 (IoU $_i^{\mathrm{I}}$  = 9.91).
DeepLabV3+-ResNet50: 2010_004951 (IoU<sub>i</sub> = 1.74).
DeepLabV3+-ResNet34: 2007_007498 (IoU<sub>i</sub> = 6.92).
DeepLabV3+-ResNet18: 2010_004951 (IoU<sub>i</sub> = 4.81).
Figure 35: Worst-case images: PASCAL VOC 1 / 3. Left: image. Middle: label. Right: prediction.
DeepLabV3+-EfficientNetB0: 2010_004951 (IoU $_i^I$  = 0.35).
DeepLabV3+-MobileNetV2: 2008_008051 (IoU $_i^{\mathrm{I}}$  = 5.63).
DeepLabV3+-MobileViTV2: 2008_008051 (IoU $_i^{\mathrm{I}}$  = 4.42).
UPerNet-ResNet101: 2010_004951 (IoU<sub>i</sub> = 1.57).
UPerNet-ConvNeXt: 2010_001327 (IoU<sub>i</sub> = 9.69).
UPerNet-MiTB4: 2011_003182 (IoU<sub>i</sub> = 7.39).
Figure 36: Worst-case images: PASCAL VOC 2/3. Left: image. Middle: label. Right: prediction.
SegFormer-MiTB4: 2011_003182 (IoU<sub>i</sub> = 7.36).
SegFormer-MiTB2: 2011_003182 (IoU<sub>i</sub> = 7.34).
DeepLabV3-ResNet101: 2008_008051 (IoU<sub>i</sub> = 4.72).
PSPNet-ResNet101: 2008_000763 (IoU<sub>i</sub> = 9.23).
UNet-ResNet101: 2008_008051 (IoU<sub>i</sub> = 5.04).
Figure 37: Worst-case images: PASCAL VOC 3/3. Left: image. Middle: label. Right: prediction.
G.9 PASCAL Context
Table 22: Results: PASCAL Context. Red: the best for each metric. Green: the worst for each metric.
Method	Backbone	mIoU1	mIoUq	mIoU5	mIoU1
mIoUC	mIoUCq	mIoUC5	mIoUC1
mIoUD	mAcc	Acc
DeepLabV3+	ResNet101	67.16 ± 0.04	51.85 ± 0.03	27.15 ± 0.19	14.81 ± 1.23
55.89 ± 0.07	34.93 ± 0.06	3.24 ± 0.29	0.00 ± 0.00
56.49 ± 0.10	67.66 ± 0.27	81.76 ± 0.09
DeepLabV3+	ResNet50	65.42 ± 0.07	49.91 ± 0.08	24.97 ± 0.14	12.35 ± 0.75
53.95 ± 0.18	33.12 ± 0.11	2.57 ± 0.05	0.00 ± 0.00
54.50 ± 0.08	65.74 ± 0.23	80.57 ± 0.07
DeepLabV3+	ResNet34	61.92 ± 0.13	45.52 ± 0.19	18.58 ± 0.63	3.60 ± 0.97
49.49 ± 0.20	27.84 ± 0.10	0.40 ± 0.04	0.00 ± 0.00
48.82 ± 0.07	60.90 ± 0.15	77.45 ± 0.08
DeepLabV3+	ResNet18	60.39 ± 0.11	43.87 ± 0.12	16.93 ± 0.40	2.76 ± 0.69
47.37 ± 0.26	26.09 ± 0.26	0.45 ± 0.03	0.00 ± 0.00
46.59 ± 0.33	58.58 ± 0.39	76.40 ± 0.12
DeepLabV3+	EfficientNetB0	60.95 ± 0.13	44.33 ± 0.24	17.24 ± 0.65	2.85 ± 0.92
48.13 ± 0.05	26.56 ± 0.11	0.42 ± 0.08	0.00 ± 0.00
47.90 ± 0.18	60.02 ± 0.04	77.31 ± 0.16
DeepLabV3+	MobileNetV2	59.30 ± 0.10	42.89 ± 0.14	16.84 ± 0.33	3.19 ± 0.29
46.00 ± 0.18	24.87 ± 0.23	0.22 ± 0.04	0.00 ± 0.00
45.64 ± 0.11	57.77 ± 0.21	75.72 ± 0.08
DeepLabV3+	MobileViTV2	62.48 ± 0.26	45.77 ± 0.34	18.91 ± 0.62	5.23 ± 0.78
49.96 ± 0.21	28.15 ± 0.18	0.39 ± 0.12	0.00 ± 0.00
50.38 ± 0.36	61.95 ± 0.40	78.53 ± 0.21
UPerNet	ResNet101	66.42 ± 0.10	50.85 ± 0.09	25.78 ± 0.36	13.32 ± 0.92
55.04 ± 0.10	34.48 ± 0.10	3.05 ± 0.07	0.00 ± 0.00
55.65 ± 0.10	66.19 ± 0.17	81.24 ± 0.06
UPerNet	ConvNeXt	70.36 ± 0.09	55.78 ± 0.07	31.27 ± 0.29	17.88 ± 0.80
60.17 ± 0.02	39.11 ± 0.04	6.05 ± 0.11	0.00 ± 0.00
61.33 ± 0.06	71.84 ± 0.06	84.09 ± 0.06
UPerNet	MiTB4	69.83 ± 0.03	54.57 ± 0.02	29.50 ± 0.20	16.10 ± 0.35
58.55 ± 0.12	37.15 ± 0.14	3.83 ± 0.34	0.00 ± 0.00
60.50 ± 0.13	70.90 ± 0.11	84.07 ± 0.10
SegFormer	MiTB4	69.54 ± 0.05	54.40 ± 0.07	29.72 ± 0.23	16.11 ± 0.90
58.29 ± 0.14	36.97 ± 0.22	3.63 ± 0.28	0.00 ± 0.00
60.21 ± 0.14	70.75 ± 0.20	83.90 ± 0.04
SegFormer	MiTB2	67.24 ± 0.03	51.69 ± 0.03	26.39 ± 0.07	12.76 ± 0.30
55.26 ± 0.04	34.02 ± 0.04	2.06 ± 0.01	0.00 ± 0.00
56.73 ± 0.06	67.33 ± 0.10	82.20 ± 0.02
DeepLabV3	ResNet101	65.84 ± 0.04	50.57 ± 0.11	26.41 ± 0.46	14.25 ± 0.71
54.33 ± 0.17	33.94 ± 0.13	2.96 ± 0.12	0.00 ± 0.00
55.80 ± 0.08	66.18 ± 0.22	81.41 ± 0.07
PSPNet	ResNet101	65.86 ± 0.11	50.54 ± 0.14	26.09 ± 0.10	13.81 ± 0.43
54.35 ± 0.03	33.89 ± 0.08	3.03 ± 0.10	0.00 ± 0.00
55.52 ± 0.11	66.03 ± 0.07	81.23 ± 0.06
UNet	ResNet101	65.55 ± 0.07	50.60 ± 0.03	26.27 ± 0.40	14.08 ± 0.90
53.88 ± 0.15	33.07 ± 0.09	2.80 ± 0.07	0.00 ± 0.00
51.11 ± 0.33	63.92 ± 0.41	78.74 ± 0.08
DeepLabV3+-ResNet101  
min: 0.00, max: 100.00  
mean: 67.16, std: 18.97  
skew: -0.12, kurtosis: -0.55
DeepLabV3+-ResNet50 min: 0.22, max: 100.00 mean: 65.42, std: 19.31 skew: -0.08, kurtosis: -0.55
DeepLabV3+-ResNet34  
min: 0.00, max: 100.00  
mean: 61.92, std: 20.22  
skew: -0.07, kurtosis: -0.38
DeepLabV3+-ResNet18  
min: 0.00, max: 100.00  
mean: 60.39, std: 20.48  
skew: -0.01, kurtosis: -0.41
DeepLabV3+-EfficientNetB0 min: 0.00, max: 100.00 mean: 60.96, std: 20.41 skew: 0.00, kurtosis: -0.45
DeepLabV3+-MobileNetV2 min: 0.00, max: 100.00 mean: 59.30, std: 20.42 skew: 0.04, kurtosis: -0.45
DeepLabV3+-MobileViTV2 min:0.00,max:100.00 mean:62.48,std:20.64 skew:-0.05,kurtosis:-0.51
UPerNet-ResNet101 min: 0.19, max: 100.00 mean: 66.42, std: 19.39 skew: -0.11, kurtosis: -0.55
UPerNet-ConvNeXt min: 0.09, max: 100.00 mean: 70.36, std: 18.12 skew: -0.25, kurtosis: -0.42
UPerNet-MiTB4 min:0.03,max:100.00 mean:69.83,std:18.82 skew:-0.23,kurtosis:-0.52
SegFormer-MiTB4 min: 0.01, max: 100.00 mean: 69.54, std: 18.76 skew: -0.21, kurtosis: -0.51
SegFormer-MiTB2 min: 0.00, max: 100.00 mean: 67.24, std: 19.42 skew: -0.14, kurtosis: -0.50
DeepLabV3-ResNet101 min: 0.24, max: 100.00 mean: 65.84, std: 19.19 skew: -0.07, kurtosis: -0.55
PSPNet-ResNet101 min: 0.88, max: 100.00 mean: 65.86, std: 19.20 skew: -0.08, kurtosis: -0.55
UNet-ResNet101 min: 0.81, max: 100.00 mean: 65.55, std: 18.74 skew: -0.09, kurtosis: -0.45
Figure 38: Histograms: PASCAL Context.
DeepLabV3+-ResNet101: 2009_004291 (IoU $_i^{\mathrm{I}} = 0.00$ ).
DeepLabV3+-ResNet50: 2009_004291 (IoU<sub>i</sub> = 0.00).
DeepLabV3+-ResNet34: 2008_003763 (IoU<sub>i</sub> = 0.00).
DeepLabV3+-ResNet18: 2008_001111 (IoU<sub>i</sub> = 0.00).
Figure 39: Worst-case images: PASCAL Context 1 / 3. Left: image. Middle: label. Right: prediction.
DeepLabV3+-EfficientNetB0: 2008_000670 (IoU<sub>i</sub> = 0.00).
DeepLabV3+-MobileNetV2: 2008_003763 (IoU<sub>i</sub> = 0.00).
DeepLabV3+-MobileViTV2: 2008_005748 (IoU $_i^{\mathrm{I}} = 0.00$ ).
UPerNet-ResNet101: 2008_004736 (IoU<sub>i</sub> = 0.00).
UPerNet-ConvNeXt: 2008_005046 (IoU<sub>i</sub> = 0.00).
Figure 40: Worst-case images: PASCAL Context 2 / 3. Left: image. Middle: label. Right: prediction.
UPerNet-MiTB4: 2008_005046 (IoU<sub>i</sub> = 0.00).
SegFormer-MiTB4: 2008_005046 (IoU<sub>i</sub> = 0.00).
SegFormer-MiTB2: 2009_000304 (IoU<sub>i</sub> = 0.00).
DeepLabV3-ResNet101: 2008_004736 (IoU<sub>i</sub> = 0.00).
PSPNet-ResNet101: 2009_004291 (IoU<sub>i</sub> = 0.00).
Figure 41: Worst-case images: PASCAL Context 3 / 3. Left: image. Middle: label. Right: prediction.
UNet-ResNet101: 2010_000379 (IoU<sub>i</sub> = 0.08).
G.10 DeepGlobe Land
Table 23: Results: DeepGlobe Land. Red: the best for each metric. Green: the worst for each metric.
Method	Backbone	mIoU1	mIoU1q	mIoU1b	mIoU1
mIoUC	mIoUCq	mIoUC5	mIoUC1
mIoUD	mAcc	Acc
DeepLabV3+	ResNet101	57.26 ± 0.46	45.96 ± 0.49	28.81 ± 0.72	21.66 ± 1.18
53.76 ± 0.40	31.62 ± 0.28	2.80 ± 0.18	0.00 ± 0.00
71.27 ± 0.21	82.80 ± 0.16	87.21 ± 0.08
DeepLabV3+	ResNet50	57.13 ± 0.12	46.02 ± 0.29	30.83 ± 1.13	22.38 ± 2.81
53.63 ± 0.17	31.48 ± 0.18	2.53 ± 0.02	0.00 ± 0.00
71.44 ± 0.07	82.94 ± 0.15	87.23 ± 0.09
DeepLabV3+	ResNet34	56.84 ± 0.32	45.83 ± 0.48	28.28 ± 1.87	18.53 ± 1.97
53.50 ± 0.36	31.42 ± 0.44	2.39 ± 0.17	0.00 ± 0.00
70.76 ± 0.24	82.93 ± 0.24	86.85 ± 0.15
DeepLabV3+	ResNet18	56.37 ± 0.05	45.34 ± 0.13	28.60 ± 0.48	19.41 ± 0.30
53.01 ± 0.09	30.87 ± 0.14	2.64 ± 0.16	0.00 ± 0.00
70.76 ± 0.12	82.66 ± 0.24	86.81 ± 0.05
DeepLabV3+	EfficientNetB0	58.15 ± 0.10	47.16 ± 0.13	30.29 ± 0.20	18.44 ± 0.09
55.03 ± 0.07	33.14 ± 0.22	2.84 ± 0.06	0.00 ± 0.00
71.67 ± 0.21	83.40 ± 0.22	87.52 ± 0.13
DeepLabV3+	MobileNetV2	56.61 ± 0.60	45.45 ± 0.77	28.86 ± 1.23	19.43 ± 0.69
53.25 ± 0.72	31.28 ± 0.92	2.69 ± 0.20	0.00 ± 0.00
70.34 ± 0.39	81.87 ± 0.64	86.79 ± 0.19
DeepLabV3+	MobileViTV2	58.64 ± 0.29	47.48 ± 0.37	31.49 ± 1.15	23.23 ± 3.07
55.44 ± 0.42	33.25 ± 0.36	2.74 ± 0.15	0.00 ± 0.00
72.86 ± 0.51	83.69 ± 0.54	87.97 ± 0.25
UPerNet	ResNet101	56.47 ± 0.10	44.93 ± 0.25	26.45 ± 0.90	17.53 ± 4.38
52.72 ± 0.20	30.42 ± 0.21	2.31 ± 0.12	0.00 ± 0.00
70.73 ± 0.17	81.81 ± 0.15	86.99 ± 0.03
UPerNet	ConvNeXt	58.85 ± 0.21	47.69 ± 0.31	30.95 ± 0.52	19.93 ± 0.80
55.83 ± 0.29	33.68 ± 0.32	2.75 ± 0.24	0.00 ± 0.00
72.91 ± 0.18	83.18 ± 0.29	88.01 ± 0.10
UPerNet	MiTB4	59.31 ± 0.20	47.99 ± 0.25	31.13 ± 1.04	21.49 ± 2.50
56.42 ± 0.28	34.35 ± 0.40	3.28 ± 0.06	0.00 ± 0.00
73.21 ± 0.11	83.75 ± 0.14	88.15 ± 0.10
SegFormer	MiTB4	59.38 ± 0.24	48.20 ± 0.28	31.93 ± 0.35	21.28 ± 2.94
56.44 ± 0.30	34.26 ± 0.36	3.23 ± 0.03	0.00 ± 0.00
73.36 ± 0.25	84.03 ± 0.47	88.17 ± 0.14
SegFormer	MiTB2	59.41 ± 0.35	48.08 ± 0.38	30.98 ± 0.60	20.27 ± 0.98
56.21 ± 0.28	34.14 ± 0.27	3.24 ± 0.20	0.00 ± 0.00
73.22 ± 0.22	83.69 ± 0.13	88.18 ± 0.11
DeepLabV3	ResNet101	57.31 ± 0.13	46.07 ± 0.24	28.46 ± 1.26	20.32 ± 1.43
53.80 ± 0.09	31.70 ± 0.10	2.53 ± 0.15	0.00 ± 0.00
71.39 ± 0.22	82.53 ± 0.25	87.30 ± 0.20
PSPNet	ResNet101	57.03 ± 0.63	45.84 ± 0.67	29.44 ± 0.94	21.82 ± 2.49
53.45 ± 0.71	31.38 ± 0.71	2.48 ± 0.15	0.00 ± 0.00
71.02 ± 0.60	82.39 ± 0.75	87.07 ± 0.30
UNet	ResNet101	56.65 ± 0.08	45.31 ± 0.13	28.04 ± 0.28	19.69 ± 0.48
53.13 ± 0.07	30.98 ± 0.09	2.48 ± 0.17	0.00 ± 0.00
70.71 ± 0.30	82.44 ± 0.05	86.97 ± 0.14
DeepLabV3+-ResNet101  
min: 21.66, max: 99.33  
mean: 57.26, std: 14.55  
skew: 0.15, kurtosis: -0.38
DeepLabV3+-ResNet50 min: 24.58, max: 99.70 mean: 57.13, std: 14.49 skew: 0.23, kurtosis: -0.46
DeepLabV3+-ResNet34  
min: 19.96, max: 99.22  
mean: 56.84, std: 14.28  
skew: 0.16, kurtosis: -0.26
DeepLabV3+-ResNet18 min: 19.41, max: 99.30 mean: 56.38, std: 14.31 skew: 0.16, kurtosis: -0.31
DeepLabV3+-EfficientNetB0 min: 18.44, max: 99.72 mean: 58.16, std: 14.27 skew: 0.18, kurtosis: -0.21
DeepLabV3+-MobileNetV2 min: 19.43, max: 99.56 mean: 56.61, std: 14.37 skew: 0.18, kurtosis: -0.36
DeepLabV3+-MobileViTV2 min: 25.82, max: 99.94 mean: 58.64, std: 14.31 skew: 0.14, kurtosis: -0.54
UPerNet-ResNet101 min: 19.71, max: 99.35 mean: 56.47, std: 14.84 skew: 0.11, kurtosis: -0.26
UPerNet-ConvNeXt min: 19.93, max: 99.87 mean: 58.85, std: 14.36 skew: 0.10, kurtosis: -0.39
UPerNet-MiTB4 min: 21.49, max: 99.84 mean: 59.31, std: 14.49 skew: 0.09, kurtosis: -0.47
SegFormer-MiTB4 min: 23.32, max: 99.88 mean: 59.38, std: 14.44 skew: 0.14, kurtosis: -0.51
SegFormer-MiTB2 min: 20.27, max: 99.96 mean: 59.40, std: 14.53 skew: 0.05, kurtosis: -0.48
DeepLabV3-ResNet101 min: 20.32, max: 99.41 mean: 57.31, std: 14.51 skew: 0.14, kurtosis: -0.31
PSPNet-ResNet101 min:22.43,max:99.13 mean:57.03,std:14.41 skew:0.22,kurtosis:-0.37
UNet-ResNet101 min: 19.68, max: 99.31 mean: 56.65, std: 14.72 skew: 0.17, kurtosis: -0.38
Figure 42: Histograms: DeepGlobe Land.
DeepLabV3+-ResNet101: 161109_sat (IoU<sub>i</sub> = 20.01).
DeepLabV3+-ResNet50: 161109_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 19.01)$
DeepLabV3+-ResNet34: 143794_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 15.76)$
DeepLabV3+-ResNet18: 161109_sat (IoU $_i^{\mathrm{I}}$  = 19.00).
Figure 43: Worst-case images: DeepGlobe Land 1 / 3. Left: image. Middle: label. Right: prediction.
DeepLabV3+-EfficientNetB0: 161109_sat (IoU $_i^{\mathrm{I}} = 18.34$ ).
DeepLabV3+-MobileNetV2: 161109_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 18.47)$
DeepLabV3+-MobileViTV2: 161109_sat (IoU $_i^{\mathrm{I}}$  = 18.96).
UPerNet-ResNet101: 143794_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 11.45)$
UPerNet-ConvNeXt: 161109_sat (IoU<sub>i</sub> = 19.05).
Figure 44: Worst-case images: DeepGlobe Land 2 / 3. Left: image. Middle: label. Right: prediction.
UPerNet-MiTB4: 161109_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 19.22)$
SegFormer-MiTB4: 161109_sat (IoU<sub>i</sub> = 18.89).
SegFormer-MiTB2: 161109_sat (IoU<sub>i</sub> = 19.43).
DeepLabV3-ResNet101: 161109_sat (IoU $_i^{\mathrm{I}}$  = 18.88).
PSPNet-ResNet101: 161109_sat (IoU $_i^{\mathrm{I}} = 19.00$ ).
Figure 45: Worst-case images: DeepGlobe Land 3 / 3. Left: image. Middle: label. Right: prediction.
UNet-ResNet101: 161109_sat (IoU<sub>i</sub> = 19.15).
G.11 DeepGlobe Road