CutMix Data Augmentation: Techniques to Improve the Performance of Object Detection Models

Table of contents

The principle of CutMix data enhancement

Advantages and Disadvantages of CutMix Data Augmentation

What is CutMix data augmentation

CutMix is a data enhancement technology that enriches the diversity of the data set and improves the robustness and generalization ability of the model by mixing two random samples in proportion and distributing the classification results in proportion.

Specifically, CutMix first randomly generates a cropping frame, then crops out the corresponding position in picture A, and uses the region (ROI) at the corresponding position in picture B to put it into the cropped area in picture A to form a new sample. When calculating the loss, the cut-out positions in area A are randomly filled with the regional pixel values of other data in the training set, and the classification results are distributed according to a certain proportion.

Similar to Mixup, CutMix also mixes samples by interpolating the two images proportionally. The difference is that CutMix mixes images by cutting partial areas and then patching them, so there will be no unnatural mixing of the images.

The principle of CutMix data enhancement

If $X_{A}$ and $X_{B}$ are two different training samples, $ALREADY}$ and $Y_{B}$ Their corresponding label values respectively, CutMix needs to generate new training samples and corresponding labels: $X\bar{}$ and $Y\bar{}$ , the formula is as follows:

$X\tilde{} = M\odot X_{A} + (1-M)\odot X_{B}$

$Y\tilde{} = \lambda Y_{A} + (1-\lambda )Y_{B}$

Among them, $M\in\left \{0,1 \right \}^{w*h}$ is a binary mask used to subtract some areas and fill them, $\odot$ is the multiplication of images, and 1 means that all elements are The binary mask of 1, $\lambda$ , belongs to the Beta distribution like Mixup: λ∼Beta(α,α). If α=1, then λ obeys the uniform distribution of (0, 1).

In order to sample the binary mask, first sample the bounding box B = (rx, ry, rw, rh) of the clipping area, which is used to indicate the clipping area for samples xA and xB. In the paper, the rectangular mask M is sampled (the length and width are proportional to the sample size), and the bounding box sampling formula of the clipping area is as follows:

$\Upsilon _{x}\sim Unif(0,W),\Upsilon _{w} = W\sqrt{1-\lambda }$

$\Upsilon _{y}\sim Unif(0,H),\Upsilon _{h} = H\sqrt{1-\lambda }$

Ensure that the proportion of the cropping area is $\frac{\Upsilon _{w}\Upsilon _{h}}{WH} = 1-\lambda$ . After determining the cropping area B, set the cropping area B in the binary mask to 0 and set the other areas to 1. The mask sampling is completed, then the clipping area B in sample A is removed, the clipping area B in sample B is clipped and then filled into sample A.

Advantages and Disadvantages of CutMix Data Augmentation

advantage:

Can generate more challenging training samples than Mixup because it uses more difficult-to-predict parts of the image to train the model.
It can generate smoother decision boundaries and help improve the generalization performance of the model.
It can improve the diversity of data enhancement and reduce the risk of overfitting.

shortcoming:

Implementation requires certain skills, such as selecting appropriate parameters and adjusting the loss function. If implemented incorrectly, it can degrade the performance of your model.
In practical applications, the CutMix method may need to be adjusted and optimized based on specific data sets and tasks.

Summarize

CutMix is a very effective data augmentation method that can improve the performance and generalization ability of target detection models. It can be used in various object detection tasks and helps improve the accuracy and robustness of the model.