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The ADE20K semantic segmentation dataset contains more than 20K scene-centric images exhaustively annotated with pixel-level objects and object parts labels. There are totally 150 semantic categories, which include stuffs like sky, road, grass, and discrete objects like person, car, bed. Source: Cooperative Image Segmentation and Restoration in Adverse Environmental Conditions Image Source: https://groups.csail.mit.edu/vision/datasets/ADE20K/
SA-1B consists of 11M diverse, high resolution, licensed, and privacy protecting images and 1.1B high-quality segmentation masks. Source: Segment Anything Image Source: Segment Anything
For the details of the work, the readers are refer to the paper "Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection" (FPHB), T-ITS 2019. You can find the paper in https://www.researchgate.net/publication/330244656_Feature_Pyramid_and_Hierarchical_Boosting_Network_for_Pavement_Crack_Detection or https://arxiv.org/abs/1901.06340. The pavement crack datasets used in paper, crack detection results on each datasets, trained model, and crack annotation tool are stored in Google Drive, One Drive, and Daidu Yunpan extract code: jviq.
This is the first general Underwater Image Instance Segmentation (UIIS) dataset containing 4,628 images for 7 categories with pixel-level annotations for underwater instance segmentation task
The ROBUST-MIS dataset was made available to support the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019, part of the Endoscopic Vision Challenge associated with MICCAI. The goal of this challenge is the benchmarking of algorithms for medical instrument detection and segmentation with a specific emphasis on robustness and generalization capabilities of the methods. The challenge is based on the biggest annotated data set made available in the field at the time, comprising 10,000 annotated images that have been extracted from a total of 30 surgical procedures from three different surgery types. Data acquisition took place during daily routine procedures in: - rectal resection, - proctocolectomy and - UNKNOWN SURGERY (will be made public after the docker submission deadline) surgeries in the Heidelberg University Hospital, Department of Surgery. The resulting laparoscopic video data was then anonymized by excluding parts of the video displaying parts outside the abdomen. A training case encompasses a 10 second video snippet in form of 250 endoscopic image frames and a reference annotation for the last frame. In the annotated frame a “0” indicates the absence of a medical instrument and numbers “1”, “2“, ... represent different instances of medical instruments. The test cases are identical in format but do not include a reference annotation. UPDATE: While all training and test cases were used for the multiple instance detection task, cases not showing an instrument in the image were removed from training and test sets for the binary and multiple instance segmentation tasks.
Manual crown delineation of individual trees in two countries: Denmark and Finland. ---------- Denmark data---------- : * More than 20k individual trees growing in different landscapes. * Annotation masks are paired with aerial photographs with a spatial resolution of 20cm. * Images contain RGB and near-infrared bands and were taken in summer 2018. * Image credits: Danish Agency for Data Supply and Infrastructure (https://sdfi.dk/) ---------- Finland data---------: * More than 4k individual tree crowns in random sampling regions * Annotation masks are paired with aerial photographs containing near-infrared, green, and blue bands with a spatial resolution of 50cm * Image credits: National Survey of Finland (https://www.maanmittauslaitos.fi/en/maps-and-spatial-data) Tasks: * Segmentation of individual tree crowns * Transfer learning/domain adaptation between datasets with different visual semantics, band compositions, forest species, etc. Paper: https://doi.org/10.1093/pnasnexus/pgad076
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing and quantifying ICH has important clinical implications, in a bleed-dependent manner. While deep learning techniques are widely used in medical image segmentation and have been applied to the ICH segmentation task, existing public ICH datasets do not support the multi-class segmentation problem. To address this, we develop the Brain Hemorrhage Segmentation Dataset (BHSD), which provides a 3D multi-class ICH dataset containing 192 volumes with pixel-level annotations and 2200 volumes with slice-level annotations across five categories of ICH. To demonstrate the utility of the dataset, we formulate a series of supervised and semi-supervised ICH segmentation tasks. We provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset.
The dataset offers tag and mask annotations for image-text pairs from the CC3M validation set. Tag annotations denote words that aptly describe the relationship between the image and the corresponding text. These annotations provide valuable insights into the semantic connection between each pair's visual and textual elements.
MMFlood is remote sensing dataset derived from Sentinel-1 (VV-VH), MapZen (DEM) and OpenStreetMap (Hydrography). It provides a complete and well-rounded set of data specifically designed for flood events, focusing on three main features: worldwide distribution, manually validated annotations and multiple modalities. 1748 pairs of SAR images and pixel-level annotations exclusively based around flooded areas and taking into account 95 flood events in 42 different countries around the world, in a time span ranging from 2014 to 2021. Image size <2000x2000, image resolution of 20m . Manually verified flood events across the world with raw SAR acquisitions derived from Sentinel-1. Includion of Digital Elevation Model (DEM) images.
CrackForest Dataset is an annotated road crack image database which can reflect urban road surface condition in general. If you use this crack image dataset, we appreciate it if you cite an appropriate subset of the following papers: @article{shi2016automatic, title={Automatic road crack detection using random structured forests}, author={Shi, Yong and Cui, Limeng and Qi, Zhiquan and Meng, Fan and Chen, Zhensong}, journal={IEEE Transactions on Intelligent Transportation Systems}, volume={17}, number={12}, pages={3434--3445}, year={2016}, publisher={IEEE} } @inproceedings{cui2015pavement, title={Pavement Distress Detection Using Random Decision Forests}, author={Cui, Limeng and Qi, Zhiquan and Chen, Zhensong and Meng, Fan and Shi, Yong}, booktitle={International Conference on Data Science}, pages={95--102}, year={2015}, organization={Springer} } 2.License. The dataset is made available for non-commercial research purposes only. 3.History. Version 1.0 (2015/09/29) initial version
We present the CrackVision12k dataset, a collection of 12,000 crack images derived from 13 publicly available crack datasets. The individual datasets were too small to effectively train a deep learning model. Moreover, the masks in each dataset were annotated using different standards, so unifying the annotations was necessary. To achieve this, we applied various image processing techniques to each dataset to create masks that follow a consistent standard. Crack datasets inherently suffer from class imbalance. To mitigate this issue, we selected images containing crack pixels of more than 5000 pixels and applied data augmentation techniques such as Gaussian noise and rotation. Finally, there is a corresponding refined ground truth for each crack image across the dataset to ensure uniformity and reliability. The 13 datasets we combined are as follows: Aigle-RN, ESAR, LCMS, CRACK500, CrackLS315, CRKWH100, CrackTree260, DeepCrack, GAPS384, Masonry, Stone331, CFD, and SDNet2018.
The George B. Moody PhysioNet Challenges are annual competitions that invite participants to develop automated approaches for addressing important physiological and clinical problems. The 2024 Challenge invites teams to develop algorithms for digitizing and classifying electrocardiograms (ECGs) captured from images or paper printouts. Despite the recent advances in digital ECG devices, physical or paper ECGs remain common, especially in the Global South. These physical ECGs document the history and diversity of cardiovascular diseases (CVDs), and algorithms that can digitize and classify these images have the potential to improve our understanding and treatment of CVDs, especially for underrepresented and underserved populations.
From my knowledge, the dataset used in the project is the largest crack segmentation dataset so far. It contains around 11.200 images that are merged from 12 available crack segmentation datasets. The name prefix of each image is assigned to the corresponding dataset name that the image belong to. There're also images with no crack pixel, which could be filtered out by the file name pattern "noncrack*" All the images are resized to the size of (448, 448). The two folders images and masks contain all the images. The two folders train and test contain training and testing images splitted from the two above folder. The splitting is stratified so that the proportion of each dataset in the train and test folder are similar. Citation Note: please cite the corresponding papers when using these datasets. CRACK500: @inproceedings{zhang2016road, title={Road crack detection using deep convolutional neural network}, author={Zhang, Lei and Yang, Fan and Zhang, Yimin Daniel and Zhu, Ying Julie}, booktitle={Image Processing (ICIP), 2016 IEEE International Conference on}, pages={3708--3712}, year={2016}, organization={IEEE} }' . @article{yang2019feature, title={Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection}, author={Yang, Fan and Zhang, Lei and Yu, Sijia and Prokhorov, Danil and Mei, Xue and Ling, Haibin}, journal={arXiv preprint arXiv:1901.06340}, year={2019} } GAPs384: @inproceedings{eisenbach2017how, title={How to Get Pavement Distress Detection Ready for Deep Learning? A Systematic Approach.}, author={Eisenbach, Markus and Stricker, Ronny and Seichter, Daniel and Amende, Karl and Debes, Klaus and Sesselmann, Maximilian and Ebersbach, Dirk and Stoeckert, Ulrike and Gross, Horst-Michael}, booktitle={International Joint Conference on Neural Networks (IJCNN)}, pages={2039--2047}, year={2017} } CFD: @article{shi2016automatic, title={Automatic road crack detection using random structured forests}, author={Shi, Yong and Cui, Limeng and Qi, Zhiquan and Meng, Fan and Chen, Zhensong}, journal={IEEE Transactions on Intelligent Transportation Systems}, volume={17}, number={12}, pages={3434--3445}, year={2016}, publisher={IEEE} } AEL: @article{amhaz2016automatic, title={Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection.}, author={Amhaz, Rabih and Chambon, Sylvie and Idier, J{'e}r{^o}me and Baltazart, Vincent} } cracktree200: @article{zou2012cracktree, title={CrackTree: Automatic crack detection from pavement images}, author={Zou, Qin and Cao, Yu and Li, Qingquan and Mao, Qingzhou and Wang, Song}, journal={Pattern Recognition Letters}, volume={33}, number={3}, pages={227--238}, year={2012}, publisher={Elsevier} } https://github.com/alexdonchuk/cracks_segmentation_dataset https://github.com/yhlleo/DeepCrack https://github.com/ccny-ros-pkg/concreteIn_inpection_VGGF (Citing from https://github.com/khanhha/crack_segmentation.)
LLM-Seg40K dataset contains 14K images in total. The dataset is divided into training, validation, and test sets, containing 11K, 1K, and 2K images respectively. For the training split, each image has 3.95 questions on average and the average question question length is 15.2 words. The training set contains 1458 different categories in total.
Extension of the PASTIS benchmark with radar and optical image time series.
We construct the first large-scale dataset, USIS10K, for the underwater salient instance segmentation task, which contains 10,632 images and pixel-level annotations of 7 categories. As far as we know, this is the largest salient instance segmentation dataset, and includes Class-Agnostic and Multi-Class labels simultaneously. If you use this dataset in your work, please consider to cite: @inproceedings{ lian2024icml, title={Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset}, author={Shijie Lian and Ziyi Zhang and Hua Li and Wenjie Li and Laurence Tianruo Yang and Sam Kwong and Runmin Cong}, booktitle={Forty-first International Conference on Machine Learning}, year={2024}, url={https://openreview.net/forum?id=snhurpZt63} }
The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe conditions, such as Cerebral Small Vessel Diseases. The advent of 7 Tesla MRI systems has enabled the acquisition of higher spatial resolution images, making it possible to visualise such vessels in the brain. However, the lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. To address this, the SMILE-UHURA challenge was organised. This challenge, held in conjunction with the ISBI 2023, in Cartagena de Indias, Colombia, aimed to provide a platform for researchers working on related topics. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. This dataset was created through a combination of automated pre-segmentation and extensive manual refinement.
We propose a large-scale underwater instance segmentation dataset, UIIS10K, which includes 10,048 images with pixel-level annotations for 10 categories. As far as we know, this is the largest underwater instance segmentation dataset available and can be used as a benchmark for evaluating underwater segmentation methods.
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.
The researchers of Qatar University have compiled the COVID-QU-Ex dataset, which consists of 33,920 chest X-ray (CXR) images including: * 11,956 COVID-19 * 11,263 Non-COVID infections (Viral or Bacterial Pneumonia) * 10,701 Normal Ground-truth lung segmentation masks are provided for the entire dataset. This is the largest ever created lung mask dataset.
Pre-training is a strong strategy for enhancing visual models to efficiently train them with a limited number of labeled images. In semantic segmentation, creating annotation masks requires an intensive amount of labor and time, and therefore, a large-scale pre-training dataset with semantic labels is quite difficult to construct. Moreover, what matters in semantic segmentation pre-training has not been fully investigated. In this paper, we propose the Segmentation Radial Contour DataBase (SegRCDB), which for the first time applies formula-driven supervised learning for semantic segmentation. SegRCDB enables pre-training for semantic segmentation without real images or any manual semantic labels. SegRCDB is based on insights about what is important in pre-training for semantic segmentation and allows efficient pre-training. Pre-training with SegRCDB achieved higher mIoU than the pre-training with COCO-Stuff for fine-tuning on ADE-20k and Cityscapes with the same number of training images. SegRCDB has a high potential to contribute to semantic segmentation pre-training and investigation by enabling the creation of large datasets without manual annotation. The SegRCDB dataset will be released under a license that allows research and commercial use. Code is available at: https://github.com/dahlian00/SegRCDB
The ULS23 test set contains 725 lesions from 284 patients of the Radboudumc and JBZ hospitals in the Netherlands. It is intended to be used to measure the performance of 3D universal lesion segmentation models for Computed Tomography (CT). To prepare the data, radiological reports from both participating institutions where searched using NLP tools identifying patients with measurable target lesions, indicating that these lesions were clinically relevant. A random sample of patients was selected, 56.3% of which were male and with diverse scanner manufacturers. The lesions were annotated in 3D by expert radiologists with over 10 years of experience in reading oncological scans. ULS23 is an open benchmark, and we invite ongoing submissions to advance the development of future ULS models.
The AneuX morphology database includes data from 3 different data sources: AneuX, @neurIST and Aneurisk. The AneuX data consists of two portions AneuX1 and AneuX2, which have extracted by two different teams of data curators. The datasets differ in the exact case selection, but represent a very similar patient cohort (patients examined or treated for ruptured or unruptured aneurysms at the The dataset consists of totally 750 aneurysm domes. The geometries were all extracted from 3D rotational angiographies using different vessel lumen segmentation techniques.
This dataset consists of annotated images and videos of smoke resulting from prescribed burning events in Finnish boreal forests. The dataset was created to train and validate learning-based methods for wildfire detection and smoke segmentation and its effectiveness in doing so was shown in the linked studies. The data was captured as 4K (3840×2160) videos at four events in Evo, Heinola, Karkkila, and Ruokolahti, using a DJI Phantom 4 drone. Individual frames of the videos were annotated manually with bounding boxes to enable the use of the data to train and test wildfire detection models. A portion of the bounding box annotated image data was resorted and annotated at the pixel level for image segmentation model training, validation, and testing. The training and validation data were annotated automatically using the Segment Anything Model and the manually annotated bounding boxes, while a small test set was annotated with manually drawn pixelwise masks. The three parts of the dataset are stored in the three separate directories: * Boreal-Forest-Fire-Subset-A: The bounding box annotated image data * Boreal-Forest-Fire-Subset-B: 30-second 4K video clips with binary annotations for smoke * Boreal-Forest-Fire-Subset-C: The segmentation mask annotated image data In addition, a Jupyter notebook providing an example for visualising and converting the bounding box coordinates is provided in the Visualisation-And-Coordinate-Conversion-Notebook directory. Code for using the segmentation data is found in the linked Gitlab repository.
RGB-D instance segmentation box dataset. The Box-IS dataset was created to support research on human-robot collaboration with a focus on robotic manipulation tasks. It was captured using the Intel® RealSense™ Depth Camera D455, a high-performance sensor designed for depth imaging. To ensure precise depth measurements, we bypassed the default depth data processing of the sensor and performed accurate stereo matching directly from the captured left and right IR images. Employing the UniMatch technique, we derived a high-quality depth map from these stereo IR images, which was then aligned with the corresponding RGB image for a comprehensive output. The dataset was intentionally designed to encompass a broad range of scene complexities, from simple box arrangements to highly irregular configurations. This diversity ensures that it can effectively benchmark algorithms across varying levels of difficulty.
Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset
BRISC is a high-quality, expert-annotated MRI dataset curated for brain tumor segmentation and classification. It addresses common limitations in existing datasets (e.g., BraTS, Figshare), including class imbalance, narrow tumor focus, and annotation inconsistencies.
This dataset derives from Coil100. There are more than 1,1M images of 100 objects. Each object was turned on a turnable through 360 degrees to vary object pose with respect to a fixed color camera. Images of the objects were taken at pose intervals of 5 degrees. This corresponds to 72 poses per object. Then planar rotation (9 angles) and 18 scaling factors has been applied. Objects have a wide variety of complex geometric and reflectance characteristics. This augmented version of Coil100 has been design for Disentangled Representation Learning for real images, the Factors of Variations are: | Factors | # values | |----------|----------| | Object | 100 | | 3D Pose | 72 | | Rotation | 9 | | Scale | 18 | The binarized version is also available.
A fully synthetic dataset of drones generated using structured domain randomization. It contains multiple datasets generated using different styles: - Drones only - Drones and Birds - Generic Distractors - Realistic Distractors - Random Backgrounds This was done to understand how different styles affect the sim-to-real translation of faster-rcnn models (full study is done in the paper) Further, the bounds of the camera in relation to the drones is a simulation parameter that has a strong influence on the size of the generated drones in the dataset. To understand this relation, multiple datasets are generated, with different maximum camera bounds: - 20 m - 40 m - 80 m - 160 m - 320 m A study on how this affects transferability to real life datasets is conducted in the paper.
There are 537 RGB jpg images of cracks and corresponding png binary segmentation masks of crack: a training set with 300 images and a testing set with 237 images. All of the images are of a fixed size of 544 × 384 pixels. Images diversity: 1. asphalt 22%, concrete 78% 2. dirty 22.4%, rough 40%, bare 37,6% 3 The width of cracks is in ranges from 1 pixel to 180 ones in the database.
A dataset of abdominal CT studies in NifTi format from the open-source medical data repository Medical Decathlon was utilized. To expedite the partitioning process, the MONAILabel plugin of the MONAI framework within the 3D Slicer program was employed. A radiologist with 15 years of experience conducted a validation process, wherein the boundaries of the colon markup were verified on each slice. The existing colorectal cancer markings in the dataset remained unaltered. Validation by a radiologist reduced the size of the validated dataset to 122 studies. In this case, the 122 studies were categorized into three subsets based on the quality of the data: The "good" subset comprises 100 studies, while the "bad" subset contains 17 cropped studies (in which the entire colon is not visible on the image). The "bad" subset comprises five studies. Two of these studies were of poor quality and could not identify the entire colon. Two further studies involved colon stomas following surgery, while one study involved a hernia. Four studies were excluded from the set due to broken images, with axial plane images duplicated or the slice order mixed
We introduce a new style- and category-agnostic floor plan image parsing benchmark developed in collaboration with professional architectural designers. This benchmark includes 25 categories of space and adjacency labels (19 space elements and 6 adjacency elements), offering a more diverse and comprehensive representation of common design elements across various graphical styles and design categories. It sets a new standard for the level of diversity and complexity of floor plan image parsing tasks oriented towards real-world applications, far exceeding the scope of existing datasets. This benchmark is available at https://doi.org/10.7910/DVN/MDIRHE.
This data set comprises 22 fundus images with their corresponding manual annotations for the blood vessels, separated as arteries and veins. It also include labels for glaucomatous / healthy, differentiating between normal tension glaucoma (NAG) and primary open angle glaucoma (POAG). Further information about the data set is provided in our MICCAI 2018 paper: Orlando, J. I., Breda, J. B., van Keer, K., Blaschko, M. B., Blanco, P. J., & Bulant, C. A. (2018). Towards a glaucoma risk index based on simulated hemodynamics from fundus images. MICCAI 2018. Please, cite the corresponding paper in case you use this data on a scientific publication. Any commercial usage is forbidden. This data set can only be used for scientific purposes.
During the covid-19 era wearing face masks posed new challenges to face-related tasks, including facial recognition, face inpainting, expression recognition, and object removal. Mask region segmentation is a preliminary stage to tackle the occlusion issue corresponding to the face-related tasks. Existing masked face datasets are not procedure binary segmentation maps because Segmenting mask regions manually is a time-consuming operation. As a result, existing unmasking methods; synthesize training data by overlaying masks on existing face datasets. However, since these techniques rely on an artificially generated mask, their effects tend to seem unnatural. To address this issue, the masked face segmentation dataset(MFSD) provides the first public training dataset for the mask segmentation task.
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