29 datasets found

Groups: Semantic Segmentation Formats: JSON

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  • Vaihingen dataset

    The Vaihingen dataset consists of 1440 scenes with a size of 250×250 pixels. Each scene is a colour-infrared (CIR) true orthophoto and a height grid (digital surface model; DSM)...

  • Synthia

    The Synthia dataset is a large-scale urban scene understanding dataset, containing 9000 samples. It is used for semantic segmentation tasks.

  • PASCAL-5i

    Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature...

  • GTA

    The GTA dataset consists of 24966 synthetic images synthetically generated from a video game consisting of outdoor scenes with rich variety of variations in lighting and traffic...

  • NYUD-v2

    The NYUD-v2 dataset is a benchmark for indoor scene segmentation and depth estimation. It contains 1449 images with 4 tasks: semantic segmentation, depth estimation, surface...

  • Synthia→Cityscapes

    The Synthia→Cityscapes task is a domain adaptation task for semantic segmentation, where the source domain is Synthia and the target domain is Cityscapes.

  • GTA5 and SYNTHIA

    The dataset used in the paper is GTA5 and SYNTHIA, which are used for domain adaptive semantic segmentation (DASS).

  • ADE20K Dataset

    The ADE20K dataset is a large-scale dataset for semantic segmentation. It contains 20,000 images with 150 semantic categories, with 20,000 images for training, 2,000 images for...

  • CamVid Dataset

    CamVid dataset is a benchmark dataset for semantic segmentation. It consists of 700 images with 11 object classes.

  • Pascal VOC 2012

    The dataset used in the paper is the Pascal VOC 2012 dataset, which is a benchmark for instance segmentation. The dataset consists of 1464 images with 20 class categories and...

  • COCO Stuff

    COCO Stuff dataset is an extension of the COCO dataset, 164,000 images covering 171 classes are annotated with segmentation masks.

  • Pyramid scene parsing network

    Pyramid scene parsing network for semantic segmentation.

  • PASCAL Context

    The PASCAL Context dataset is a benchmark for multi-task learning in computer vision. It contains 10103 images with 5 tasks: semantic segmentation, human body part segmentation,...

  • CamVid

    The dataset used in the paper is a pre-trained ResNet-50 classifier, which is used for image synthesis, unpaired image-to-image translation, and feature similarity estimation.

  • CelebAMask-HQ

    CelebAMask-HQ provides the parsing map of images in CelebA-HQ down-sampled to 512 × 512, where pixel-level annotation of 19 classes, including facial components and accessories,...

  • SUN-RGBD

    The dataset is used for indoor scene understanding and contains RGB and depth images.

  • PASCAL VOC 2007

    Multi-label image recognition is a practical and challenging task compared to single-label image classification.

  • NYUv2

    Multi-task learning (MTL) research is broadly divided into two categories: one is to learn the correlation between tasks through model structures, and the other is to balance...

  • Syntagen - Harnessing Generative Models for Synthetic Visual Datasets

    The dataset is generated using a latent diffusion model, specifically Stable Diffusion 2.1, and is used for semantic segmentation tasks.

  • LoveDA

    The LoveDA dataset contains high-spatial-resolution images from three different cities, focusing on improving the generalization capability of model from different urban and...