| classification-on-indl | MobileNetV3 | #9 | Average Recall: 84.28% |
| dichotomous-image-segmentation-on-dis-te1 | MBV3 | #10 | max F-Measure: 0.669weighted F-measure: 0.595MAE: 0.083… |
| dichotomous-image-segmentation-on-dis-te2 | MBV3 | #11 | max F-Measure: 0.743weighted F-measure: 0.672MAE: 0.083… |
| dichotomous-image-segmentation-on-dis-te3 | MBV3 | #11 | max F-Measure: 0.772weighted F-measure: 0.702MAE: 0.078… |
| dichotomous-image-segmentation-on-dis-te4 | MBV3 | #13 | max F-Measure: 0.736weighted F-measure: 0.664MAE: 0.098… |
| dichotomous-image-segmentation-on-dis-vd | MBV3 | #13 | max F-Measure: 0.714weighted F-measure: 0.642MAE: 0.092… |
| image-classification-on-imagenet | MobileNet V3-Large 1.0 | #895 | Top 1 Accuracy: 75.2%Number of params: 5.4MGFLOPs: 0.438 |
| semantic-segmentation-on-cityscapes | MobileNet V3-Large 1.0 | #62 | Mean IoU (class): 72.6% |
| semantic-segmentation-on-dada-seg | MobileNetV3 (MobileNetV3small) | #25 | mIoU: 18.2 |