Online Label Smoothing

The code for the paper "Delving Deep into Label Smoothing"
I have only cleaned the code on the fine-grained datasets. Since I am not currently in school, I have not tested it. So if there are any bugs, please feel easy to contact me (zhangchbin AATT gmail Ddot com).

Citation

@ARTICLE{zhang2021delving,
  author={Zhang, Chang-Bin and Jiang, Peng-Tao and Hou, Qibin and Wei, Yunchao and Han, Qi and Li, Zhen and Cheng, Ming-Ming},
  journal={IEEE Transactions on Image Processing}, 
  title={Delving Deep into Label Smoothing}, 
  year={2021},
  volume={30},
  number={},
  pages={5984-5996},
  doi={10.1109/TIP.2021.3089942}}

Performance

Classification for fine-grained datasets

Model ensemble on CIFAR-100

TODO

supporting for Jittor
training scripts for more network and datasets
~~EfficientNet for classification~~
~~SAN network for classification~~
training code on the ImageNet
training code on the Noisy-CIFAR
adversarial attack code on the ImageNet and CIFAR
~~training code on the CIFAR~~
~~the download link of CUB-200-2011, Flowers, Cars and dogs~~

Requirements

pytorch >= 1.0
torchvision
numpy
tensorboardX
apex
tqdm
efficientnet_pytorch
SAN_network

efficientnet_pytorch
SAN network

Data Preparation

Download all datasets to the data directory, note that we modify the division for datasets as shown in files in the data directory:

You can download the images from Cars. And the lists for training and validation are in the data directory.
You can download the images from Aircrafts. And the lists are in the data directory.
You can download the images from Flowers with 102 classes. And the lists are in the data directory.
You can download the images from Standford-dogs. And the lists are in the data directory.

Train and Validate

download the ImageNet pretrained model to checkpoint
MobileNet-v2, ResNet-50, Res2Net

train the model with online label smoothing:

CUDA_VISIBLE_DEVICES=1 python main.py \
--mode train \
--pretrained_model ./checkpoint/mobilenet_v2-b0353104.pth \
--epochs 100 \
--lr 0.01 \
--arch mobilenetv2 \
--dataset cub \
--method ols \
--batch_size 64 \

(optional) test the model ensemble performance:

python main.py \
--mode ensemble \
--ensemble 'runs/mobilenetv2_cub_ols/20.pth' 'runs/mobilenetv2_cub_ols/60.pth' \
--epochs 100 \
--lr 0.01 \
--arch mobilenetv2 \
--dataset aircraft \
--method ols \
--batch_size 64 \

Train on CIFAR

```
cd cifar
sh train_cifar_imagenetresnet34.sh
sh train_cifar_resnext29_2.sh
````

Other realated implementation

Thanks to the re-implementation in Kurumi233 and ankandrew

zhangchbin/OnlineLabelSmoothing