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Image Augmentation

Experiments about data augmentation will be introduced in detail in this section. If you want to quickly experience these methods, please refer to Quick start PaddleClas in 30 miniutes, which based on CIFAR100 dataset. If you want to know the content of related algorithms, please refer to Data Augmentation Algorithm Introduction.

Catalogue

Configurations

Since hyperparameters differ from different augmentation methods. For better understanding, we list 8 augmentation configuration files in configs/DataAugment based on ResNet50. Users can train the model with tools/run.sh. The following are 3 of them.

1.1 AutoAugment

The configuration of the data augmentation method of AotoAugment is as follows. AutoAugment is converted on the uint8 data format, so its processing should be placed before the normalization operation (NormalizeImage).

      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - AutoAugment:
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''

1.2 RandAugment

The configuration of the data augmentation method of RandAugment is as follows, where the user needs to specify the parameters num_layers and magnitude, and the default values ​​are 2 and 5 respectively. RandAugment is converted on the uint8 data format, so its processing should be placed before the normalization operation (NormalizeImage).

      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - RandAugment:
            num_layers: 2
            magnitude: 5
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''

1.3 TimmAutoAugment

The configuration of the data augmentation method of TimmAutoAugment is as follows, in which the user needs to specify the parameters config_str, interpolation, and img_size. The default values ​​are rand-m9-mstd0.5-inc1 and bicubic. , 224. TimmAutoAugment is converted on the uint8 data format, so its processing should be placed before the normalization operation (NormalizeImage).

      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - TimmAutoAugment:
            config_str: rand-m9-mstd0.5-inc1
            interpolation: bicubic
            img_size: 224
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''

1.4 Cutout

The configuration of the data augmentation method of Cutout is as follows, where the user needs to specify the parameters n_holes and length, and the default values are 1 and 112 respectively. Similar to other image cropping data augmentation methods, Cutout can operate on data in uint8 format, or on data after normalization (NormalizeImage).The demo here is operated after normalization.

      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
        - Cutout:
            n_holes: 1
            length: 112

1.5 RandomErasing

The configuration of the image augmentation method of RandomErasing is as follows, where the user needs to specify the parameters EPSILON, sl, sh, r1, attempt, use_log_aspect, mode, and the default values They are 0.25, 0.02, 1.0/3.0, 0.3, 10, True, and pixel. Similar to other image cropping data augmentation methods, RandomErasing can operate on data in uint8 format, or on data after normalization (NormalizeImage).The demo here is operated after normalization.

      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
        - RandomErasing:
            EPSILON: 0.25
            sl: 0.02
            sh: 1.0/3.0
            r1: 0.3
            attempt: 10
            use_log_aspect: True
            mode: pixel

1.6 HideAndSeek

The configuration of the image augmentation method of HideAndSeek is as follows. Similar to other image cropping data augmentation methods, HideAndSeek can operate on data in uint8 format, or on data after normalization (NormalizeImage).The demo here is operated after normalization.

      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
        - HideAndSeek:

1.7 GridMask

The configuration of the image augmentation method of GridMask is as follows, where the user needs to specify the parameters d1, d2, rotate, ratio, mode, and the default values are 96, 224 respectively , 1, 0.5, 0. Similar to other image cropping data augmentation methods, HideAndSeek can operate on data in uint8 format, or on data after normalization (GridMask).The demo here is operated after normalization.

      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
        - GridMask:
            d1: 96
            d2: 224
            rotate: 1
            ratio: 0.5
            mode: 0

1.8 Mixup

The configuration of the data augmentation method of Mixup is as follows, where the user needs to specify the parameter alpha, and the default value is 0.2. Similar to other image mixing data augmentation methods, Mixup is to perform image mix on the data in each batch after the image is processed, and the mixed images and labels are put into the network for training, so it operates after image data processing (image transformation, image cropping).

      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
      batch_transform_ops:
        - MixupOperator:
            alpha: 0.2

1.9 Cutmix

The configuration of the image augmentation method of Cutmix is as follows, where the user needs to specify the parameter alpha, and the default value is 0.2. Similar to other image mixing data augmentation methods, Mixup is to perform image mix on the data in each batch after the image is processed, and the mixed images and labels are put into the network for training, so it operates after image data processing (image transformation, image cropping).

      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
      batch_transform_ops:
        - CutmixOperator:
            alpha: 0.2

1.10 Use Mixup and Cutmix at the same time

The configuration for both Mixup and Cutmix is as follows, in which the user needs to specify an additional parameter prob, which controls the probability of different data enhancements, and the default is 0.5.

      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
        - OpSampler:
            MixupOperator:
              alpha: 0.8
              prob: 0.5
            CutmixOperator:
              alpha: 1.0
              prob: 0.5

2. Start training

After you configure the training environment, similar to training other classification tasks, you only need to replace the configuration file in tools/train.sh with the configuration file of the corresponding data augmentation method.

The contents of train.sh are as follows:

python3 -m paddle.distributed.launch \
    --selected_gpus="0,1,2,3" \
    --log_dir=ResNet50_Cutout \
    tools/train.py \
        -c ./ppcls/configs/ImageNet/DataAugment/ResNet50_Cutout.yaml

Run train.sh:

sh tools/train.sh

3. Matters needing attention

  • In addition, because the label needs to be aliased when the image is aliased, the accuracy of the training data cannot be calculated. The training accuracy rate was not printed during the training process.

  • The training data is more difficult with data augmentation, so the training loss may be larger, the training set accuracy is relatively low, but it has better generalization ability, so the validation set accuracy is relatively higher.

  • After the use of data augmentation, the model may tend to be underfitting. It is recommended to reduce l2_decay for better performance on validation set.

  • hyperparameters exist in almost all agmenatation methods. Here we provide hyperparameters for ImageNet1k dataset. User may need to finetune the hyperparameters on specified dataset. More training tricks can be referred to Tricks.

If this document is helpful to you, welcome to star our project: https://github.com/PaddlePaddle/PaddleClas

4. Experiments

Based on PaddleClas, Metrics of different augmentation methods on ImageNet1k dataset are as follows.

Model Learning strategy l2 decay batch size epoch Augmentation method Top1 Acc Reference
ResNet50 0.1/cosine_decay 0.0001 256 300 Standard transform 0.7731 -
ResNet50 0.1/cosine_decay 0.0001 256 300 AutoAugment 0.7795 0.7763
ResNet50 0.1/cosine_decay 0.0001 256 300 mixup 0.7828 0.7790
ResNet50 0.1/cosine_decay 0.0001 256 300 cutmix 0.7839 0.7860
ResNet50 0.1/cosine_decay 0.0001 256 300 cutout 0.7801 -
ResNet50 0.1/cosine_decay 0.0001 256 300 gridmask 0.7785 0.7790
ResNet50 0.1/cosine_decay 0.0001 256 300 random-augment 0.7770 0.7760
ResNet50 0.1/cosine_decay 0.0001 256 300 random erasing 0.7791 -
ResNet50 0.1/cosine_decay 0.0001 256 300 hide and seek 0.7743 0.7720

note:

  • In the experiment here, for better comparison, we fixed the l2 decay to 1e-4. To achieve higher accuracy, we recommend trying to use a smaller l2 decay. Combined with data augmentaton, we found that reducing l2 decay from 1e-4 to 7e-5 can bring at least 0.3~0.5% accuracy improvement.
  • We have not yet combined different strategies or verified, whch is our future work.