| image-classification-on-imagenet | ResNeSt-269 | #299 | Top 1 Accuracy: 84.5%Number of params: 111M |
| image-classification-on-imagenet | ResNeSt-200 | #362 | Top 1 Accuracy: 83.9%Number of params: 70M |
| image-classification-on-imagenet | ResNeSt-101 | #459 | Top 1 Accuracy: 83.0%Number of params: 48M |
| image-classification-on-imagenet | ResNeSt-50 | #632 | Top 1 Accuracy: 81.13%Number of params: 27.5MGFLOPs: 5.39 |
| image-classification-on-imagenet | ResNeSt-50-fast | #658 | Top 1 Accuracy: 80.64%Number of params: 27.5MGFLOPs: 4.34 |
| instance-segmentation-on-coco | ResNeSt101 | #48 | mask AP: 43% |
| instance-segmentation-on-coco | ResNeSt-200 (multi-scale) | #103 | AP50: 70.2AP75: 51.5APS: 30.0APM: 49.6APL: 60.6 |
| instance-segmentation-on-coco-minival | ResNeSt-200 (multi-scale) | #41 | mask AP: 46.25 |
| instance-segmentation-on-coco-minival | ResNeSt-200-DCN (single-scale) | #50 | mask AP: 44.5 |
| instance-segmentation-on-coco-minival | ResNeSt-200 (single-scale) | #55 | mask AP: 44.21 |
| instance-segmentation-on-coco-minival | ResNeSt-101 (single-scale) | #63 | mask AP: 41.56 |
| object-detection-on-coco | ResNeSt-200 (multi-scale) | #63 | box mAP: 53.3AP50: 72.0AP75: 58.0APS: 35.1APM: 56.2APL: 66.8 |
| object-detection-on-coco-minival | ResNeSt-200 (multi-scale) | #65 | box AP: 52.47AP50: 71.00AP75: 57.07APS: 36.80APM: 56.36… |
| object-detection-on-coco-minival | ResNeSt-200-DCN (single-scale) | #75 | box AP: 50.91AP50: 69.53AP75: 55.40APS: 32.67APM: 54.66… |
| object-detection-on-coco-minival | ResNeSt-200 (single-scale) | #77 | box AP: 50.54AP50: 68.78AP75: 55.17APM: 54.2APL: 63.9 |
| panoptic-segmentation-on-coco-minival | PanopticFPN+ResNeSt(single-scale) | #24 | PQ: 47.9PQst: 37.0PQth: 55.1 |
| semantic-segmentation-on-ade20k | ResNeSt-200 | #144 | Validation mIoU: 48.36 |
| semantic-segmentation-on-ade20k | ResNeSt-269 | #157 | Validation mIoU: 47.60 |
| semantic-segmentation-on-ade20k | ResNeSt-101 | #164 | Validation mIoU: 46.91 |
| semantic-segmentation-on-ade20k-val | ResNeSt-200 | #60 | mIoU: 48.36 |
| semantic-segmentation-on-ade20k-val | ResNeSt-269 | #62 | mIoU: 47.60 |
| semantic-segmentation-on-ade20k-val | ResNeSt-101 | #64 | mIoU: 46.91 |
| semantic-segmentation-on-cityscapes | ResNeSt200 (Mapillary) | #14 | Mean IoU (class): 83.3% |
| semantic-segmentation-on-cityscapes-val | ResNeSt-200 | #29 | mIoU: 82.7 |
| semantic-segmentation-on-dada-seg | ResNeSt (ResNeSt-101) | #21 | mIoU: 19.99 |
| semantic-segmentation-on-pascal-context | ResNeSt-269 | #13 | mIoU: 58.9 |
| semantic-segmentation-on-pascal-context | ResNeSt-200 | #15 | mIoU: 58.4 |
| semantic-segmentation-on-pascal-context | ResNeSt-101 | #19 | mIoU: 56.5 |