Image stylization is a task that involves transforming an input image into a new image that has a different style, while preserving the content of the original image. The goal of image stylization is to create visually appealing images with a specific style or aesthetic, such as impressionism, cubism, or surrealism. It can also be used to make images more visually appealing for specific applications, such as social media or advertising.
데이터셋
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
CodeSCAN is the first large-scale and diverse dataset of coding screenshots with pixel-perfect annotations. It features: - 24 popular programming languages (according to Github) - 100 random repositories per language (with MIT, BSD-3 or WTFPL License), i.e. 2.400 repositories in total - Per repository we use 5 files, i.e. 12.000 files in total - ~100 different themes and 25 different fonts - Diverse layouts changes, such as menu bar visibility, sidebar position, output window content, etc. - Numerous realistic interactions such as searching, typing and selecting within a file, etc. Check our project page (https://a-nau.github.io/codescan/) for details.