{"url":"/method/gridmask","slug":"gridmask","name":"GridMask","full_name":"GridMask","full_name_withheld":false,"description_markdown":"**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.\r\n\r\nWe express the setting as\r\n\r\n$$\r\n\\tilde{\\mathbf{x}}=\\mathbf{x} \\times M\r\n$$\r\n\r\nwhere $\\mathbf{x} \\in R^{H \\times W \\times C}$ 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.\r\n\r\nThe 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.","description_state":"present","introduced_year":null,"introduced_by":{"title":"GridMask Data Augmentation","paper":"/paper/gridmask-data-augmentation","first_author":"Pengguang Chen","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/gridmask-data-augmentation"},"source":{"url":"https://arxiv.org/abs/2001.04086v3","title":"GridMask Data Augmentation","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Data Augmentation","url":"/methods/category/image-data-augmentation","pwc_aliases":[]}],"n_papers_tagged":3,"archive_num_papers":3,"papers_newest_first":[{"paper":null,"title":"TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene","date":"2022-10-05","arxiv_id":"2210.02287","n_code_links":0,"syntology":null},{"paper":"/paper/instance-segmentation-challenge-track","title":"Instance Segmentation Challenge Track Technical Report, VIPriors Workshop at ICCV 2021: Task-Specific Copy-Paste Data Augmentation Method for Instance Segmentation","date":"2021-10-01","arxiv_id":"2110.00470","n_code_links":1,"syntology":null},{"paper":"/paper/gridmask-data-augmentation","title":"GridMask Data Augmentation","date":"2020-01-13","arxiv_id":"2001.04086","n_code_links":7,"syntology":null}],"papers_shown":3,"tasks":[{"task":"/task/data-augmentation","name":"Data Augmentation","papers":3},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":2},{"task":"/task/automl","name":"AutoML","papers":1},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/reinforcement-learning","name":"Reinforcement Learning","papers":1},{"task":"/task/reinforcement-learning-1","name":"Reinforcement Learning (RL)","papers":1},{"task":"/task/segmentation","name":"Segmentation","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1},{"task":"/task/reinforcement-learning-2","name":"reinforcement-learning","papers":1}],"tasks_shown":10,"n_tasks":10,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":1},{"year":"2022","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/gridmask"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}