| Rank | Model |
mIoU (val) | mIoU (test) |
Paper | Code | Year |
| 1 |
TEC (ViT-B/16, 224x224, SSL+FT, mmseg) |
63.2 | 62.5 |
Towards Sustainable Self-supervised Learning
|
sail-sg/tec |
2022 |
| 2 |
SERE (ViT-B/16, 100ep, 224x224, SSL+FT) |
63.0 | 63.3 |
SERE: Exploring Feature Self-relation for Self-supervised Transformer
|
MCG-NKU/SERE |
2022 |
| 3 |
TEC (ViT-B/16, 224x224, SSL+FT) |
62.0 | – |
Towards Sustainable Self-supervised Learning
|
sail-sg/tec |
2022 |
| 4 |
MAE (ViT-B/16, 224x224, SSL+FT, mmseg) |
61.6 | 61.2 |
Masked Autoencoders Are Scalable Vision Learners
|
facebookresearch/mae · lightly-ai/lightly · open-mmlab/mmselfsup
· +55 |
2021 |
| 5 |
MAE (ViT-B/16, 224x224, SSL+FT) |
61.0 | 60.2 |
Masked Autoencoders Are Scalable Vision Learners
|
facebookresearch/mae · lightly-ai/lightly · open-mmlab/mmselfsup
· +55 |
2021 |
| 6 |
SERE (ViT-S/16, 100ep, 224x224, SSL+FT, mmseg) |
59.4 | 59.0 |
SERE: Exploring Feature Self-relation for Self-supervised Transformer
|
MCG-NKU/SERE |
2022 |
| 7 |
SERE (ViT-S/16, 100ep, 224x224, SSL+FT) |
58.9 | 57.8 |
SERE: Exploring Feature Self-relation for Self-supervised Transformer
|
MCG-NKU/SERE |
2022 |
| 8 |
RF-ConvNext-Tiny (rfmerge, P4, 224x224, SUP) |
51.3 | 51.1 |
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
|
ShangHua-Gao/G2L-search · ShangHua-Gao/RFNext |
2022 |
| 9 |
RF-ConvNext-Tiny (rfmultiple, P4, 224x224, SUP) |
50.8 | 50.5 |
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
|
ShangHua-Gao/G2L-search · ShangHua-Gao/RFNext |
2022 |
| 10 |
RF-ConvNext-Tiny (rfsingle, P4, 224x224, SUP) |
50.7 | 50.5 |
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
|
ShangHua-Gao/G2L-search · ShangHua-Gao/RFNext |
2022 |