SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)

PyTorch implementation of SnapMix | paper


    title={SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data},
    author={Shaoli Huang, Xinchao Wang, and Dacheng Tao},
    booktitle={AAAI Conference on Artificial Intelligence},


Install Package Dependencies



create a soft link to the dataset directory

CUB dataset

ln -s /your-path-to/CUB-dataset data/cub

Car dataset

ln -s /your-path-to/Car-dataset data/car

Aircraft dataset

ln -s /your-path-to/Aircraft-dataset data/aircraft


Training with Imagenet pre-trained weights

1. Baseline and Baseline+

To train a model on CUB dataset using the Resnet-50 backbone,

python # baseline

python --midlevel # baseline+

To train model on other datasets using other network backbones, you can specify the following arguments:

--netname: name of network architectures (support 4 network families: ResNet,DenseNet,InceptionV3,EfficientNet)

--dataset: dataset name

For example,

python --netname resnet18 --dataset cub # using the Resnet-18 backbone on CUB dataset

python --netname efficientnet-b0 --dataset cub # using the EfficientNet-b0 backbone on CUB dataset

python --netname inceptoinV3 --dataset aircraft # using the inceptionV3 backbone on Aircraft dataset

2. Training with mixing augmentation

Applying SnapMix in training ( we used the hyperparameter values (prob=1., beta=5) for SnapMix in most of the experiments.):

python --mixmethod snapmix --beta 5 --netname resnet50 --dataset cub # baseline

python --mixmethod snapmix --beta 5 --netname resnet50 --dataset cub --midlevel # baseline+

Applying other augmentation methods (currently support cutmix,cutout,and mixup) in training:

python --mixmethod cutmix --beta 3 --netname resnet50 --dataset cub # training with CutMix

python --mixmethod mixup --prob 0.5 --netname resnet50 --dataset cub # training with MixUp

3. Results

ResNet architecture.

Backbone Method CUB Car Aircraft
Resnet-18 Baseline 82.35% 91.15% 87.80%
Resnet-18 Baseline + SnapMix 84.29% 93.12% 90.17%
Resnet-34 Baseline 84.98% 92.02% 89.92%
Resnet-34 Baseline + SnapMix 87.06% 93.95% 92.36%
Resnet-50 Baseline 85.49% 93.04% 91.07%
Resnet-50 Baseline + SnapMix 87.75% 94.30% 92.08%
Resnet-101 Baseline 85.62% 93.09% 91.59%
Resnet-101 Baseline + SnapMix 88.45% 94.44% 93.74%
Resnet-50 Baseline+ 87.13% 93.80% 91.68%
Resnet-50 Baseline+ + SnapMix 88.70% 95.00% 93.24%
Resnet-101 Baseline+ 87.81% 93.94% 91.85%
Resnet-101 Baseline+ + SnapMix 89.32% 94.84% 94.05%

InceptionV3 architecture.

Backbone Method CUB
InceptionV3 Baseline 82.22%
InceptionV3 Baseline + SnapMix 85.54%

DenseNet architecture.

Backbone Method CUB
DenseNet121 Baseline 84.23%
DenseNet121 Baseline + SnapMix 87.42%

Training from scratch

To train a model without using ImageNet pretrained weights:

python --mixmethod snapmix --prob 0.5 --netname resnet18 --dataset cub --pretrained 0 # resnet-18 backbone

python --mixmethod snapmix --prob 0.5 --netname resnet50 --dataset cub --pretrained 0 # resnet-50 backbone

2. Results

Backbone Method CUB
Resnet-18 Baseline 64.98%
Resnet-18 Baseline + SnapMix 70.31%
Resnet-50 Baseline 66.92%
Resnet-50 Baseline + SnapMix 72.17%