GridMask is a data augmentation method that randomly removes some pixels of an input image. Unlike other methods, the region that the algorithm removes is neither a continuous region nor random pixels in dropout. Instead, the algorithm removes a region with disconnected pixel sets, as shown in the Figure.
We express the setting as
$$ \tilde{\mathbf{x}}=\mathbf{x} \times M $$
where $\mathbf{x} \in R^{H \times W \times C}$ represents the input image, $M \in$ ${0,1}^{H \times W}$ is the binary mask that stores pixels to be removed, and $\tilde{\mathbf{x}} \in R^{H \times W \times C}$ is the result produced by the algorithm. For the binary mask $M$, if $M_{i, j}=1$ we keep pixel $(i, j)$ in the input image; otherwise we remove it. GridMask is applied after the image normalization operation.
The shape of $M$ looks like a grid, as shown in the Figure . Four numbers $\left(r, d, \delta_{x}, \delta_{y}\right)$ are used to represent a unique $M$. Every mask is formed by tiling the units. $r$ is the ratio of the shorter gray edge in a unit. $d$ is the length of one unit. $\delta_{x}$ and $\delta_{y}$ are the distances between the first intact unit and boundary of the image.
Source: GridMask Data AugmentationPaper  Code  Results  Date  Stars 

Task  Papers  Share 

Semantic Segmentation  2  33.33% 
Instance Segmentation  1  16.67% 
Test  1  16.67% 
Object Detection  1  16.67% 
Reinforcement Learning (RL)  1  16.67% 
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🤖 No Components Found  You can add them if they exist; e.g. Mask RCNN uses RoIAlign 