SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 24: Results: DeepGlobe Road. 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	64.59 ± 0.88	53.15 ± 1.19	26.09 ± 2.10	8.50 ± 1.45
64.59 ± 0.88	53.15 ± 1.19	26.09 ± 2.10	8.00 ± 1.63
65.60 ± 0.45	88.68 ± 0.52	98.22 ± 0.01
DeepLabV3+	ResNet50	63.86 ± 0.07	52.56 ± 0.08	25.95 ± 0.36	6.67 ± 1.47
63.86 ± 0.07	52.56 ± 0.08	25.95 ± 0.36	6.00 ± 1.41
64.59 ± 0.08	88.34 ± 0.14	98.16 ± 0.01
DeepLabV3+	ResNet34	63.49 ± 0.17	51.77 ± 0.22	23.13 ± 0.50	4.63 ± 0.97
63.49 ± 0.17	51.77 ± 0.22	23.13 ± 0.50	4.00 ± 0.82
64.51 ± 0.06	88.21 ± 0.11	98.16 ± 0.01
DeepLabV3+	ResNet18	62.74 ± 0.12	50.90 ± 0.22	22.52 ± 0.93	5.16 ± 1.01
62.74 ± 0.12	50.90 ± 0.22	22.52 ± 0.93	4.67 ± 1.25
63.87 ± 0.08	87.85 ± 0.11	98.12 ± 0.00
DeepLabV3+	EfficientNetB0	64.29 ± 0.09	53.49 ± 0.10	28.82 ± 0.11	13.88 ± 0.58
64.29 ± 0.09	53.49 ± 0.10	28.82 ± 0.11	13.67 ± 0.47
65.14 ± 0.07	89.16 ± 0.07	98.16 ± 0.00
DeepLabV3+	MobileNetV2	62.25 ± 0.24	50.47 ± 0.33	22.58 ± 0.73	5.95 ± 0.78
62.25 ± 0.24	50.47 ± 0.33	22.58 ± 0.73	5.33 ± 0.47
63.47 ± 0.12	87.94 ± 0.27	98.08 ± 0.00
DeepLabV3+	MobileViTV2	64.65 ± 0.13	53.58 ± 0.24	27.50 ± 0.83	12.66 ± 1.71
64.65 ± 0.13	53.58 ± 0.24	27.50 ± 0.83	12.33 ± 1.70
65.55 ± 0.05	88.81 ± 0.10	98.21 ± 0.01
UPerNet	ResNet101	63.06 ± 0.52	51.03 ± 1.05	22.61 ± 3.41	5.12 ± 3.13
63.06 ± 0.52	51.03 ± 1.05	22.61 ± 3.41	4.67 ± 3.30
64.55 ± 0.26	87.68 ± 0.56	98.19 ± 0.01
UPerNet	ConvNeXt	66.71 ± 0.04	55.82 ± 0.12	29.37 ± 0.82	10.13 ± 2.14
66.71 ± 0.04	55.82 ± 0.12	29.37 ± 0.82	9.67 ± 1.89
67.65 ± 0.05	89.88 ± 0.10	98.33 ± 0.00
UPerNet	MiTB4	65.05 ± 0.87	53.73 ± 1.21	26.65 ± 2.79	10.51 ± 5.42
65.05 ± 0.87	53.73 ± 1.21	26.65 ± 2.79	10.00 ± 5.66
66.37 ± 0.64	88.90 ± 0.55	98.27 ± 0.03
SegFormer	MiTB4	64.27 ± 1.15	52.90 ± 1.59	25.69 ± 2.96	8.70 ± 4.76
64.27 ± 1.15	52.90 ± 1.59	25.69 ± 2.96	8.33 ± 4.50
65.61 ± 0.87	88.61 ± 0.72	98.23 ± 0.04
SegFormer	MiTB2	64.74 ± 0.02	53.81 ± 0.03	28.66 ± 0.22	13.74 ± 1.37
64.74 ± 0.02	53.81 ± 0.03	28.66 ± 0.22	13.00 ± 1.41
65.70 ± 0.09	89.44 ± 0.12	98.19 ± 0.00
DeepLabV3	ResNet101	61.82 ± 2.12	49.95 ± 2.66	22.49 ± 4.08	6.34 ± 2.31
61.82 ± 2.12	49.95 ± 2.66	22.49 ± 4.08	6.00 ± 2.16
63.57 ± 1.53	88.16 ± 0.81	98.08 ± 0.09
PSPNet	ResNet101	61.13 ± 2.60	49.09 ± 3.45	21.76 ± 5.14	7.33 ± 3.03
61.13 ± 2.60	49.09 ± 3.45	21.76 ± 5.14	6.67 ± 3.09
63.19 ± 1.73	87.49 ± 1.25	98.09 ± 0.08
UNet	ResNet101	62.88 ± 0.68	50.85 ± 0.94	21.23 ± 2.00	3.29 ± 0.87
62.88 ± 0.68	50.85 ± 0.94	21.23 ± 2.00	3.00 ± 0.82
64.26 ± 0.56	86.77 ± 0.71	98.21 ± 0.00
DeepLabV3+-ResNet101  
min: 0.00, max: 90.85  
mean: 64.59, std: 14.06  
skew: -1.04, kurtosis: 1.94
DeepLabV3+-ResNet50 min: 0.00, max: 92.84 mean: 63.87, std: 14.01 skew: -1.03, kurtosis: 2.10
DeepLabV3+-ResNet34 min: 0.00, max: 90.87 mean: 63.49, std: 14.41 skew: -1.15, kurtosis: 2.16
DeepLabV3+-ResNet18 min: 0.00, max: 90.55 mean: 62.74, std: 14.54 skew: -1.06, kurtosis: 1.82
DeepLabV3+-EfficientNetB0 min: 0.00, max: 92.55 mean: 64.29, std: 13.19 skew: -0.92, kurtosis: 1.47
DeepLabV3+-MobileNetV2 min:0.00,max:90.57 mean:62.25,std:14.48 skew:-1.00,kurtosis:1.66
DeepLabV3+-MobileViTV2 min: 3.65, max: 92.31 mean: 64.65, std: 13.61 skew: -1.00, kurtosis: 1.63
UPerNet-ResNet101 min: 0.00, max: 91.65 mean: 63.06, std: 14.70 skew: -0.98, kurtosis: 1.63
UPerNet-ConvNeXt min: 0.00, max: 93.26 mean: 66.71, std: 13.49 skew: -1.18, kurtosis: 2.49
UPerNet-MiTB4 min:0.00,max:93.21 mean:65.05,std:13.72 skew:-1.07,kurtosis:1.82
SegFormer-MiTB4 min: 0.00, max: 92.73 mean: 64.27, std: 13.81 skew: -1.06, kurtosis: 1.77
SegFormer-MiTB2 min: 0.00, max: 92.51 mean: 64.74, std: 13.45 skew: -0.97, kurtosis: 1.60
DeepLabV3-ResNet101 min: 0.00, max: 90.61 mean: 61.82, std: 14.47 skew: -0.95, kurtosis: 1.49
PSPNet-ResNet101 min: 0.00, max: 89.29 mean: 61.13, std: 14.42 skew: -0.86, kurtosis: 1.17
UNet-ResNet101 min: 0.00, max: 91.50 mean: 62.88, std: 14.85 skew: -1.10, kurtosis: 2.04
Figure 46: Histograms: DeepGlobe Road.
DeepLabV3+-ResNet101: 167220_sat (IoU $_i^{\mathrm{I}} = 0.00$ ).
DeepLabV3+-ResNet50: 167220_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
DeepLabV3+-ResNet34: 106741_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
DeepLabV3+-ResNet18: 122985_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
DeepLabV3+-EfficientNetB0: 167220_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
Figure 47: Worst-case images: DeepGlobe Road 1 / 3. Left: image. Middle: label. Right: prediction.
DeepLabV3+-MobileNetV2: 122985_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
DeepLabV3+-MobileViTV2: 167220_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
UPerNet-ResNet101: 157077_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
UPerNet-ConvNeXt: 247410_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
UPerNet-MiTB4: 167220_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
Figure 48: Worst-case images: DeepGlobe Road 2 / 3. Left: image. Middle: label. Right: prediction.
SegFormer-MiTB4: 167220_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
SegFormer-MiTB2: 167220_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
DeepLabV3-ResNet101: 167220_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
PSPNet-ResNet101: 167220_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
UNet-ResNet101: 122985_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
Figure 49: Worst-case images: DeepGlobe Road 3 / 3. Left: image. Middle: label. Right: prediction.
G.12 DeepGlobe Building
Table 25: Results: DeepGlobe Building. Red: the best for each metric. Green: the worst for each metric.
Method	Backbone	mIoU1	mIoU1q	mIoU5	mIoU1
mIoUC	mIoUCq	mIoUC5	mIoUC1
mIoUD	mAcc	Acc
DeepLabV3+	ResNet101	77.54 ± 0.18	59.40 ± 0.33	0.12 ± 0.11	0.00 ± 0.00
77.54 ± 0.18	59.40 ± 0.33	0.12 ± 0.11	0.00 ± 0.00
80.94 ± 0.03	93.66 ± 0.11	96.94 ± 0.01
DeepLabV3+	ResNet50	77.24 ± 0.19	59.04 ± 0.38	0.15 ± 0.18	0.00 ± 0.00
77.24 ± 0.19	59.04 ± 0.38	0.15 ± 0.18	0.00 ± 0.00
80.58 ± 0.06	93.44 ± 0.10	96.88 ± 0.02
DeepLabV3+	ResNet34	75.73 ± 0.30	55.86 ± 0.58	0.00 ± 0.00	0.00 ± 0.00
75.73 ± 0.30	55.86 ± 0.58	0.00 ± 0.00	0.00 ± 0.00
79.70 ± 0.17	93.29 ± 0.11	96.71 ± 0.04
DeepLabV3+	ResNet18	75.37 ± 0.45	55.56 ± 0.90	0.00 ± 0.00	0.00 ± 0.00
75.37 ± 0.45	55.56 ± 0.90	0.00 ± 0.00	0.00 ± 0.00
79.25 ± 0.15	92.98 ± 0.08	96.64 ± 0.03
DeepLabV3+	EfficientNetB0	74.12 ± 1.04	52.91 ± 1.94	0.00 ± 0.00	0.00 ± 0.00
74.12 ± 1.04	52.91 ± 1.94	0.00 ± 0.00	0.00 ± 0.00
80.01 ± 0.22	93.61 ± 0.05	96.75 ± 0.04
DeepLabV3+	MobileNetV2	74.66 ± 1.09	54.34 ± 2.08	0.00 ± 0.00	0.00 ± 0.00
74.66 ± 1.09	54.34 ± 2.08	0.00 ± 0.00	0.00 ± 0.00
79.20 ± 0.04	93.26 ± 0.10	96.61 ± 0.01
DeepLabV3+	MobileViTV2	74.19 ± 0.62	52.89 ± 1.21	0.00 ± 0.00	0.00 ± 0.00
74.19 ± 0.62	52.89 ± 1.21	0.00 ± 0.00	0.00 ± 0.00
80.58 ± 0.10	93.73 ± 0.03	96.86 ± 0.02
UPerNet	ResNet101	77.45 ± 0.14	59.39 ± 0.34	0.15 ± 0.13	0.00 ± 0.00
77.45 ± 0.14	59.39 ± 0.34	0.15 ± 0.13	0.00 ± 0.00
80.85 ± 0.14	93.46 ± 0.07	96.94 ± 0.02
UPerNet	ConvNeXt	79.21 ± 0.22	62.63 ± 0.54	2.83 ± 0.96	0.00 ± 0.00
79.21 ± 0.22	62.63 ± 0.54	2.83 ± 0.96	0.00 ± 0.00
81.68 ± 0.07	94.04 ± 0.00	97.06 ± 0.02
UPerNet	MiTB4	76.73 ± 1.12	57.28 ± 2.40	0.00 ± 0.00	0.00 ± 0.00
76.73 ± 1.12	57.28 ± 2.40	0.00 ± 0.00	0.00 ± 0.00
81.97 ± 0.16	94.15 ± 0.15	97.11 ± 0.02
SegFormer	MiTB4	77.02 ± 0.92	58.21 ± 2.06	0.27 ± 0.39	0.00 ± 0.00
77.02 ± 0.92	58.21 ± 2.06	0.27 ± 0.39	0.00 ± 0.00
81.60 ± 0.24	93.98 ± 0.18	97.05 ± 0.03
SegFormer	MiTB2	75.77 ± 0.03	55.67 ± 0.04	0.00 ± 0.00	0.00 ± 0.00
75.77 ± 0.03	55.67 ± 0.04	0.00 ± 0.00	0.00 ± 0.00
81.26 ± 0.02	93.91 ± 0.06	96.99 ± 0.01
DeepLabV3	ResNet101	77.21 ± 0.09	59.62 ± 0.56	1.51 ± 1.64	0.00 ± 0.00
77.21 ± 0.09	59.62 ± 0.56	1.51 ± 1.64	0.00 ± 0.00
79.89 ± 0.36	93.36 ± 0.28	96.74 ± 0.07
PSPNet	ResNet101	77.13 ± 0.30	59.09 ± 0.55	0.37 ± 0.28	0.00 ± 0.00
77.13 ± 0.30	59.09 ± 0.55	0.37 ± 0.28	0.00 ± 0.00
80.32 ± 0.31	93.48 ± 0.33	96.83 ± 0.04
UNet	ResNet101	76.93 ± 0.31	58.11 ± 0.67	0.00 ± 0.00	0.00 ± 0.00
76.93 ± 0.31	58.11 ± 0.67	0.00 ± 0.00	0.00 ± 0.00
80.87 ± 0.12	93.77 ± 0.13	96.92 ± 0.01
DeepLabV3+-ResNet101  
min: 0.00, max: 100.00  
mean: 77.55, std: 21.81  
skew: -1.92, kurtosis: 4.17
DeepLabV3+-ResNet50 min: 0.00, max: 100.00 mean: 77.24, std: 21.99 skew: -1.90, kurtosis: 4.06
DeepLabV3+-ResNet34 min: 0.00, max: 100.00 mean: 75.73, std: 23.30 skew: -1.79, kurtosis: 3.22
DeepLabV3+-ResNet18  
min: 0.00, max: 100.00  
mean: 75.37, std: 23.35  
skew: -1.75, kurtosis: 3.09
DeepLabV3+-EfficientNetB0 min: 0.00, max: 100.00 mean: 74.12, std: 23.95 skew: -1.71, kurtosis: 2.71
DeepLabV3+-MobileNetV2 min:0.00,max:100.00 mean:74.66,std:23.25 skew:-1.71,kurtosis:2.99
DeepLabV3+-MobileViTV2 min:0.00,max:100.00 mean:74.19,std:24.10 skew:-1.74,kurtosis:2.81
UPerNet-ResNet101 min: 0.00, max: 100.00 mean: 77.45, std: 21.95 skew: -1.91, kurtosis: 4.07
UPerNet-ConvNeXt min: 0.00, max: 100.00 mean: 79.21, std: 20.45 skew: -2.13, kurtosis: 5.50
UPerNet-MiTB4 min:0.00,max:100.00 mean:76.73,std:22.51 skew:-1.94,kurtosis:3.88
SegFormer-MiTB4 min: 0.00, max: 66.67 mean: 51.68, std: 14.45 skew: -1.95, kurtosis: 4.41
SegFormer-MiTB2 min: 0.00, max: 100.00 mean: 75.77, std: 24.10 skew: -1.89, kurtosis: 3.37
DeepLabV3-ResNet101 min: 0.00, max: 100.00 mean: 77.21, std: 21.09 skew: -1.88, kurtosis: 4.34
PSPNet-ResNet101 min: 0.00, max: 100.00 mean: 77.13, std: 21.42 skew: -1.87, kurtosis: 4.08
UNet-ResNet101 min: 0.00, max: 100.00 mean: 76.94, std: 22.53 skew: -1.92, kurtosis: 3.93
Figure 50: Histograms: DeepGlobe Building.
DeepLabV3+-ResNet101: Khartoum_1069_sat (IoU<sub>i</sub> = 0.00).
DeepLabV3+-ResNet50: Khartoum_1069_sat (IoU $_i^{\mathrm{I}} = 0.00$ ).
DeepLabV3+-ResNet34: Khartoum_1069_sat (IoU<sub>i</sub> = 0.00).
DeepLabV3+-ResNet18: Khartoum_1069_sat (IoU $_i^{\mathrm{I}} = 0.00$ ).
Figure 51: Worst-case images: DeepGlobe Building 1 / 3. Left: image. Middle: label. Right: prediction.
DeepLabV3+-EfficientNetB0: Khartoum_1069_sat (IoU $_i^{\mathrm{I}} = 0.00$ ).
DeepLabV3+-MobileNetV2: Khartoum_1069_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
DeepLabV3+-MobileViTV2: Khartoum_1107_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
UPerNet-ResNet101: Khartoum_1069_sat (IoU<sub>i</sub> = 0.00).
UPerNet-ConvNeXt: Khartoum_1069_sat (IoU<sub>i</sub> = 0.00).
Figure 52: Worst-case images: DeepGlobe Building 2 / 3. Left: image. Middle: label. Right: prediction.
UPerNet-MiTB4: Khartoum_1107_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
SegFormer-MiTB4: Khartoum_1108_sat (IoU $_i^{\mathrm{I}} = 0.00$ ).
SegFormer-MiTB2: Khartoum_1108_sat (IOU $_i^{\mathrm{I}} = 0.00$ ).
DeepLabV3-ResNet101: Khartoum_1069_sat (IoU $_i^{\mathrm{I}} = 0.00$ ).
PSPNet-ResNet101: Khartoum_1069_sat (IoU<sub>i</sub> = 0.00).
UNet-ResNet101: Khartoum_1069_sat  $(\mathrm{IoU}_i^{\mathrm{I}} = 0.00)$
Figure 53: Worst-case images: DeepGlobe Building 3 / 3. Left: image. Middle: label. Right: prediction.