| Training Techniques | Cosine Annealing, Random Erasing |
|---|---|
| Architecture | Convolution, ReLU, Batch Normalization, SelecSLS Block, Global Average Pooling, Dropout, Dense Connections |
| ID | selecsls42b |
| SHOW MORE |
| Training Techniques | Cosine Annealing, Random Erasing |
|---|---|
| Architecture | Convolution, ReLU, Batch Normalization, SelecSLS Block, Global Average Pooling, Dropout, Dense Connections |
| ID | selecsls60 |
| SHOW MORE |
| Training Techniques | Cosine Annealing, Random Erasing |
|---|---|
| Architecture | Convolution, ReLU, Batch Normalization, SelecSLS Block, Global Average Pooling, Dropout, Dense Connections |
| ID | selecsls60b |
| SHOW MORE |
SelecSLS uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
To load a pretrained model:
import timm
m = timm.create_model('selecsls42b', pretrained=True)
m.eval()
Replace the model name with the variant you want to use, e.g. selecsls42b. You can find the IDs in the model summaries at the top of this page.
You can follow the timm recipe scripts for training a new model afresh.
@article{Mehta_2020,
title={XNect},
volume={39},
ISSN={1557-7368},
url={http://dx.doi.org/10.1145/3386569.3392410},
DOI={10.1145/3386569.3392410},
number={4},
journal={ACM Transactions on Graphics},
publisher={Association for Computing Machinery (ACM)},
author={Mehta, Dushyant and Sotnychenko, Oleksandr and Mueller, Franziska and Xu, Weipeng and Elgharib, Mohamed and Fua, Pascal and Seidel, Hans-Peter and Rhodin, Helge and Pons-Moll, Gerard and Theobalt, Christian},
year={2020},
month={Jul}
}
Image Classification
| BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
|---|---|---|---|---|
| ImageNet | selecsls60b | Top 1 Accuracy | 78.41% | # 156 |
| Top 5 Accuracy | 94.18% | # 156 | ||
| ImageNet | selecsls60 | Top 1 Accuracy | 77.99% | # 168 |
| Top 5 Accuracy | 93.83% | # 168 | ||
| ImageNet | selecsls42b | Top 1 Accuracy | 77.18% | # 192 |
| Top 5 Accuracy | 93.39% | # 192 |
