| Training Techniques | SGD with Momentum, Weight Decay |
|---|---|
| Architecture | Batch Normalization, Convolution, Global Average Pooling, Res2NeXt Block, ReLU |
| ID | res2next50 |
| SHOW MORE |
Res2Net is an image model that employs a variation on ResNeXt bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
To load a pretrained model:
import timm
m = timm.create_model('res2next50', pretrained=True)
m.eval()
Replace the model name with the variant you want to use, e.g. res2next50. 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{Gao_2021,
title={Res2Net: A New Multi-Scale Backbone Architecture},
volume={43},
ISSN={1939-3539},
url={http://dx.doi.org/10.1109/TPAMI.2019.2938758},
DOI={10.1109/tpami.2019.2938758},
number={2},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
year={2021},
month={Feb},
pages={652–662}
}
Image Classification
| BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
|---|---|---|---|---|
| ImageNet | res2next50 | Top 1 Accuracy | 78.24% | # 161 |
| Top 5 Accuracy | 93.91% | # 161 |
