Switchable Normalization is a normalization technique that is able to learn different normalization operations for different normalization layers in a deep neural network in an end-to-end manner.
- 2018/7/9: We would like to explain the merit behind SN. See html preview or this blog (in Chinese). More trained models and the code of object detection will be released soon!
- 2018/7/4: Model zoo updated!
- 2018/7/2: The code of image classification and a pretrained model on ImageNet are released.
This repository provides imagenet classification and object detection results and models trained with Switchable Normalization:
@article{SwitchableNorm,
title={Differentiable Learning-to-Normalize via Switchable Normalization},
author={Ping Luo and Jiamin Ren and Zhanglin Peng},
journal={arXiv:1806.10779},
year={2018}
}
Comparisons of top-1 accuracies on the validation set of ImageNet, by using ResNet50 trained with SN, BN, and GN in different batch size settings. The bracket (·, ·) denotes (#GPUs,#samples per GPU). In the bottom part, “GN-BN” indicates the difference between the accuracies of GN and BN. The “-” in (8, 1) of BN indicates it does not converge.
| (8,32) | (8,16) | (8,8) | (8,4) | (8,2) | (1,16) | (1,32) | (8,1) | (1,8) | |
| BN | 76.4 | 76.3 | 75.2 | 72.7 | 65.3 | 76.2 | 76.5 | – | 75.4 |
| GN | 75.9 | 75.8 | 76.0 | 75.8 | 75.9 | 75.9 | 75.8 | 75.5 | 75.5 |
| SN | 76.9 | 76.7 | 76.7 | 75.9 | 75.6 | 76.3 | 76.6 | 75.0* | 75.9 |
| GN−BN | -0.5 | -0.5 | 0.8 | 3.1 | 10.6 | -0.3 | -0.7 | – | 0.1 |
| SN−BN | 0.5 | 0.4 | 1.5 | 3.2 | 10.3 | 0.1 | 0.1 | – | 0.5 |
| SN−GN | 1.0 | 0.9 | 0.7 | 0.1 | -0.3 | 0.4 | 0.8 | -0.5 | 0.4 |
- Install PyTorch
- Clone the repo:
git clone https://github.com/switchablenorms/Switchable-Normalization.git
- python packages
- pytorch>=0.4.0
- torchvision>=0.2.1
- tensorboardX
- pyyaml
- Download the ImageNet dataset and put them into the
{repo_root}/data/imagenet.- move validation images to labeled subfolders
- To do this, you can use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh
- move validation images to labeled subfolders
./train_val.sh configs/config_resnetv1sn50_cosine.yaml
Download the pretrained models from Model Zoo and put them into the {repo_root}/data/pretrained_model
./test.sh configs/config_resnetv1sn50_cosine.yaml
We provide models pretrained with SN on ImageNet, and compare to those pretrained with BN as reference. If you use these models in research, please cite the SN paper.
| Model | Top-1* | Top-5* | Epochs | LR Scheduler | Weight Decay | Download |
|---|---|---|---|---|---|---|
| ResNet50v2+SN (8,32) | 77.57% | 93.65% | 120 | warmup + cosine lr | 1e-4 | [Google Drive] [Baidu Pan] |
| ResNet50v1+SN (8,32) | 77.49% | 93.32% | 120 | warmup + cosine lr | 1e-4 | [Google Drive] [Baidu Pan] |
| ResNet50v1+SN (8,32) | 76.92% | 93.26% | 100 | Initial lr=0.1 decay=0.1 steps[30,60,90,10] | 1e-4 | [Google Drive] [Baidu Pan] |
| ResNet50v1+BN | 75.20% | 92.20% | -- | stepwise decay | -- | [TensorFlow models] |
| ResNet50v1+BN | 76.00% | 92.98% | -- | stepwise decay | -- | [PyTorch Vision] |
| ResNet50v1+BN | 75.30% | 92.20% | -- | stepwise decay | -- | [MSRA] |
| ResNet50v1+BN | 75.99% | 92.98% | -- | stepwise decay | -- | [FB Torch] |
*single-crop validation accuracy on ImageNet (a 224x224 center crop from resized image with shorter side=256)
When evaluation, download them and put them into the {repo_root}/data/pretrained_model.
All materials in this repository are released under the CC-BY-NC 4.0 LICENSE.