TASK
DATASET
MODEL
METRIC NAME
METRIC VALUE
GLOBAL RANK
EXTRA DATA
REMOVE
Semantic Segmentation
ADE20K
ResNeSt-200
Validation mIoU
48.36
# 121
Semantic Segmentation
ADE20K
ResNeSt-101
Validation mIoU
46.91
# 142
Semantic Segmentation
ADE20K
ResNeSt-269
Validation mIoU
47.60
# 134
Semantic Segmentation
ADE20K val
ResNeSt-200
mIoU
48.36
# 58
Semantic Segmentation
ADE20K val
ResNeSt-269
mIoU
47.60
# 61
Semantic Segmentation
ADE20K val
ResNeSt-101
mIoU
46.91
# 64
Semantic Segmentation
Cityscapes test
ResNeSt200 (Mapillary)
Mean IoU (class)
83.3%
# 11
Semantic Segmentation
Cityscapes val
ResNeSt-200
mIoU
82.7
# 23
Instance Segmentation
COCO minival
ResNeSt-200-DCN (single-scale)
mask AP
44.5
# 48
Panoptic Segmentation
COCO minival
PanopticFPN+ResNeSt(single-scale)
PQ
47.9
# 19
Panoptic Segmentation
COCO minival
PanopticFPN+ResNeSt(single-scale)
PQth
55.1
# 17
Panoptic Segmentation
COCO minival
PanopticFPN+ResNeSt(single-scale)
PQst
37.0
# 16
Object Detection
COCO minival
ResNeSt-200-DCN (single-scale)
box AP
50.91
# 67
Object Detection
COCO minival
ResNeSt-200-DCN (single-scale)
AP50
69.53
# 22
Object Detection
COCO minival
ResNeSt-200-DCN (single-scale)
AP75
55.40
# 15
Object Detection
COCO minival
ResNeSt-200-DCN (single-scale)
APS
32.67
# 14
Object Detection
COCO minival
ResNeSt-200-DCN (single-scale)
APM
54.66
# 14
Object Detection
COCO minival
ResNeSt-200-DCN (single-scale)
APL
65.83
# 16
Object Detection
COCO minival
ResNeSt-200 (multi-scale)
box AP
52.47
# 59
Object Detection
COCO minival
ResNeSt-200 (multi-scale)
AP50
71.00
# 13
Object Detection
COCO minival
ResNeSt-200 (multi-scale)
AP75
57.07
# 10
Object Detection
COCO minival
ResNeSt-200 (multi-scale)
APS
36.80
# 10
Object Detection
COCO minival
ResNeSt-200 (multi-scale)
APM
56.36
# 11
Object Detection
COCO minival
ResNeSt-200 (multi-scale)
APL
66.29
# 15
Instance Segmentation
COCO minival
ResNeSt-200 (single-scale)
mask AP
44.21
# 53
Instance Segmentation
COCO minival
ResNeSt-101 (single-scale)
mask AP
41.56
# 62
Instance Segmentation
COCO minival
ResNeSt-200 (multi-scale)
mask AP
46.25
# 38
Object Detection
COCO minival
ResNeSt-200 (single-scale)
box AP
50.54
# 69
Object Detection
COCO minival
ResNeSt-200 (single-scale)
AP50
68.78
# 25
Object Detection
COCO minival
ResNeSt-200 (single-scale)
AP75
55.17
# 17
Object Detection
COCO minival
ResNeSt-200 (single-scale)
APM
54.2
# 15
Object Detection
COCO minival
ResNeSt-200 (single-scale)
APL
63.9
# 20
Instance Segmentation
COCO test-dev
ResNeSt101
mask AP
43%
# 47
Object Detection
COCO test-dev
ResNeSt-200 (multi-scale)
box mAP
53.3
# 58
Object Detection
COCO test-dev
ResNeSt-200 (multi-scale)
AP50
72.0
# 21
Object Detection
COCO test-dev
ResNeSt-200 (multi-scale)
AP75
58.0
# 29
Object Detection
COCO test-dev
ResNeSt-200 (multi-scale)
APS
35.1
# 20
Object Detection
COCO test-dev
ResNeSt-200 (multi-scale)
APM
56.2
# 22
Object Detection
COCO test-dev
ResNeSt-200 (multi-scale)
APL
66.8
# 17
Instance Segmentation
COCO test-dev
ResNeSt-200 (multi-scale)
AP50
70.2
# 11
Instance Segmentation
COCO test-dev
ResNeSt-200 (multi-scale)
AP75
51.5
# 10
Instance Segmentation
COCO test-dev
ResNeSt-200 (multi-scale)
APS
30.0
# 9
Instance Segmentation
COCO test-dev
ResNeSt-200 (multi-scale)
APM
49.6
# 8
Instance Segmentation
COCO test-dev
ResNeSt-200 (multi-scale)
APL
60.6
# 10
Semantic Segmentation
DADA-seg
ResNeSt (ResNeSt-101)
mIoU
19.99
# 21
Image Classification
ImageNet
ResNeSt-50-fast
Top 1 Accuracy
80.64%
# 591
Image Classification
ImageNet
ResNeSt-50-fast
Number of params
27.5M
# 587
Image Classification
ImageNet
ResNeSt-50-fast
GFLOPs
4.34
# 202
Image Classification
ImageNet
ResNeSt-101
Top 1 Accuracy
83.0%
# 403
Image Classification
ImageNet
ResNeSt-101
Number of params
48M
# 673
Image Classification
ImageNet
ResNeSt-200
Top 1 Accuracy
83.9%
# 319
Image Classification
ImageNet
ResNeSt-200
Number of params
70M
# 745
Image Classification
ImageNet
ResNeSt-269
Top 1 Accuracy
84.5%
# 267
Image Classification
ImageNet
ResNeSt-269
Number of params
111M
# 828
Image Classification
ImageNet
ResNeSt-50
Top 1 Accuracy
81.13%
# 563
Image Classification
ImageNet
ResNeSt-50
Number of params
27.5M
# 587
Image Classification
ImageNet
ResNeSt-50
GFLOPs
5.39
# 230
Semantic Segmentation
PASCAL Context
ResNeSt-269
mIoU
58.9
# 15
Semantic Segmentation
PASCAL Context
ResNeSt-200
mIoU
58.4
# 16
Semantic Segmentation
PASCAL Context
ResNeSt-101
mIoU
56.5
# 19