SA-1B consists of 11M diverse, high resolution, licensed, and privacy protecting images and 1.1B high-quality segmentation masks.
90 PAPERS • NO BENCHMARKS YET
Multimodal material segmentation (MCubeS) dataset contains 500 sets of images from 42 street scenes. Each scene has images for four modalities: RGB, angle of linear polarization (AoLP), degree of linear polarization (DoLP), and near-infrared (NIR). The dataset provides annotated ground truth labels for both material and semantic segmentation for every pixel. The dataset is divided training set with 302 image sets, validation set with 96 image sets, and test set with 102 image sets. Each image has 1224 x 1024 pixels and a total of 20 class labels per pixel.
10 PAPERS • 1 BENCHMARK
The Pascal Panoptic Parts dataset consists of annotations for the part-aware panoptic segmentation task on the PASCAL VOC 2010 dataset. It is created by merging scene-level labels from PASCAL-Context with part-level labels from PASCAL-Part
7 PAPERS • 2 BENCHMARKS
MARIDA (Marine Debris Archive) is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Material, Waves, Wakes, Foam, dissimilar water types (i.e., Clear, Turbid Water, Sediment-Laden Water, Shallow Water), and Clouds. MARIDA is an open-access dataset which enables the research community to explore the spectral behaviour of certain floating materials, sea state features and water types, to develop and evaluate Marine Debris detection solutions based on artificial intelligence and deep learning architectures, as well as satellite pre-processing pipelines. Although it is designed to be beneficial for several machine learning tasks, it primarily aims to benchmark weakly supervised pixel-level semantic segmentation learning methods.
6 PAPERS • 1 BENCHMARK
The EntitySeg dataset contains 33,227 images with high-quality mask annotations. Compared with existing dataets, there are three distinct properties in EntitySeg. First, 71.25% and 86.23% of the images are of high resolution with at least 2000px×2000px and 1000px×1000px which is more consistent with current digital imaging trends. Second, the dataset is open-world and is not limited to predefined classes. Third, the mask annotation along the boundaries are more accurate than existing datasets.
5 PAPERS • NO BENCHMARKS YET
This dataset contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The dataset is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included.
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This dataset contains images from Sentinel-2 satellites taken before and after a wildfire. The ground truth masks are provided by the California Department of Forestry and Fire Protection and they are mapped on the images. The dataset is designed to do binary semantic segmentation of burned vs unburned areas.
EBHI-Seg is a dataset containing 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer.
Stack of 2D gray images of glass fiber-reinforced polyamide 66 (GF-PA66) 3D X-ray Computed Tomography (XCT) specimen.
The image set contains 180 high-resolution color microscopic images of human duodenum adenocarcinoma HuTu 80 cell populations obtained in an in vitro scratch assay (for the details of the experimental protocol, we refer to (Liang et al., 2007)). Briefly, cells were seeded in 12-well culture plates ($20 \times 10^3$ cells per well) and grown to form a monolayer with 85\% or more confluency. Then the cell monolayer was scraped in a straight line using a pipette tip ($200 \mu L$). The debris was removed by washing with a growth medium and the medium in wells was replaced. The scratch areas were marked to obtain the same field during the image acquisition. Images of the scratches were captured immediately following the scratch formation, as well as after 24, 48 and 72 h of cultivation.
1 PAPER • 1 BENCHMARK
We present a comprehensive dataset comprising a vast collection of raw mineral samples for the purpose of mineral recognition. The dataset encompasses more than 5,000 distinct mineral species and incorporates subsets for zero-shot and few-shot learning. In addition to the samples themselves, some entries in the dataset are accompanied by supplementary natural language descriptions, size measurements, and segmentation masks. For detailed information on each sample, please refer to the minerals_full.csv file.
Panoramic Video Panoptic Segmentation Dataset is a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving. The dataset has labels for 28 semantic categories and 2,860 temporal sequences that were captured by five cameras mounted on autonomous vehicles driving in three different geographical locations, leading to a total of 100k labeled camera images.
This dataset for the semantic segmentation of potholes and cracks on the road surface was assembled from 5 other datasets already publicly available, plus a very small addition of segmented images on our part. To speed up the labeling operations, we started working with depth cameras to try to automate, to some extent, this extremely time-consuming phase.
Raw-Microscopy:
Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. In this work, we introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200 diverse hyperspectral images from the HYPSO-1 mission, available in both raw and calibrated forms for scientific research in Earth observation. Moreover, 38 of these images from different countries include ground-truth labels at pixel-level totaling about 25 million spectral signatures labeled for sea/land/cloud categories. To demonstrate the potential of the dataset and its labeled subset, we have additionally optimized a deep learning model (1D Fully Convolutional Network), achieving superior performance to the current state of the art. Our dataset supports applications like super-resolution, anomaly detection, image fusion, classification, and unmixing. The complete dataset, groun
A dataset made of 3D image data and their embeddings to test TomoSAM
UV6K is a high-resolution remote sensing urban vehicle segmentation dataset.
To encourage reproducible research, a labeled MultiRAW dataset containing>7k RAW images acquired using multiple camera sensors is made publicly accessible for RAW-domain processing.
MapAI: Precision in Building Segmentation Dataset The dataset comprises 7500 training images and 1500 validation images from Denmark. The test dataset is split into two tasks, where the first task (1368 images) is to segment the buildings only using aerial images. In contrast, the second task (978 images) allows using aerial images and lidar data. All data samples have a resolution of 500x500. The aerial images are RGB images, while the lidar data are rasterized. The ground truth masks have two classes, building, and background.
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Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public datasets, comprised of full-fundus glaucoma images, associated image metadata like, optic disc segmentation, optic cup segmentation, blood vessel segmentation, and any provided per-instance text metadata like sex and age. This dataset is the largest public repository of fundus images with glaucoma.