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}
}
BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
---|---|---|---|---|
ImageNet | res2next50 | Top 1 Accuracy | 78.24% | # 161 |
Top 5 Accuracy | 93.91% | # 161 |