Papers with Code 아카이브

Image Colorization

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
1
평가표
3
보존된 평가 기록
10

데이터셋

출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.

ImageNet

The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided. * Total number of non-empty WordNet synsets: 21841 * Total number of images: 14197122 * Number of images with bounding box annotations: 1,034,908 * Number of synsets with SIFT features: 1000 * Number of images with SIFT features: 1.2 million Source: ImageNet Large Scale Visual Recognition Challenge Image Source: https://cs.stanford.edu/people/karpathy/cnnembed/

NIR2RGB VCIP Challange Dataset

#This dataset provides the VCIP 2020 Grand Challenge on the NIR Image Colorization dataset. ##You can refer to https://jchenhkg.github.io/projects/NIR2RGB_VCIP_Challenge/ for a detailed description of this dataset. ## If you think this dataset is helpful, please feel free to cite our paper: @inproceedings{yang2023cooperative, title={Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation}, author={Yang, Xingxing and Chen, Jie and Yang, Zaifeng}, booktitle={Proceedings of the 31st ACM International Conference on Multimedia}, pages={2409--2417}, year={2023} }

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.