The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets.
Res2net50
| Model | Params | MACCs | top-1 error | top-5 error |
|---|---|---|---|---|
| Res2net50 | 25.70M | 4.2 | 22.01 | 6.15 |
- Dataset used for train and validation: ImageNet (ILSVRC2012).
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
- Res2Net/Res2Net-PretrainedModels
The code is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License for Noncommercial use only. Any commercial use should get formal permission first.