Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

Reference Paper:

M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369

M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS)

General Description:

The "Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation" dataset here published should be used for Region of Interest (ROI) and/or semantic segmentation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the largest publicly available dataset of synthetic space-borne noise-free images tailored to ROI extraction and Semantic Segmentation tasks (up to our knowledge). The label of each image gives, for the Bounding Box annotations, the filename of the image, the ROI top-left corner (minimum x, minimum y) in pixels, the ROI bottom-right corner (maximum x, maximum y) in pixels, and the center point of the ROI in pixels. The annotation are taken in image reference frame with the origin located at the top-left corner of the image, positive x rightward and positive y downward. Concerning the Semantic Segmentation, RGB masks are provided. Each RGB mask correspond to a single image in both train and test dataset. The RGB images are such that the R channel corresponds to the spacecraft, the G channel corresponds to the Earth (if present), and the B channel corresponds to the background (deep space). Per each channel the pixels have non-zero value only in correspondence of the object that they represent (Tango, Earth, Deep Space). More information on the dataset split and on the label format are reported below.

Images Information:

The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints.

Labels Information:

Labels for the bounding box extraction are here provided in separated JSON files. The files are formatted per each image as in the following example:

• filename : tango_img_1 # name of the image to which the data are referred
• rol_tl : [x, y] # ROI top-left corner (minimum x, minimum y) in pixels
• roi_br : [x, y] # ROI bottom-right corner (maximum x, maximum y) in pixels
• roi_cc : [x, y] # center point of the ROI in pixels

Notice that the annotation are taken in image reference frame with the origin located at the top-left corner of the image, positive x rightward and positive y downward.To make the usage of the dataset easier, both the training set and the test set are split in two folders containing the images with earth as background and without background.

Concerning the Semantic Segmentation Labels, they are provided as RGB masks named as "filename_mask.png" where "filename" is the filename of the image of the training set or the test set to which a specific mask is referred. The RGB images are such that the R channel corresponds to the spacecraft, the G channel corresponds to the Earth (if present), and the B channel corresponds to the background (deep space). Per each channel the pixels have non-zero value only in correspondence of the object that they represent (Tango, Earth, Deep Space).

VERSION CONTROL

• v1.0: This version contains the dataset (both train and test) of full scale images with ROI annotations and RGB masks for Semantic Segmentation tasks. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images.

Note: this dataset contains the same images of the "Tango Spacecraft Wireframe Dataset Model for Line Segments Detection" v2.0 full-scale (DOI: https://doi.org/10.5281/zenodo.6372848) and also "Tango Spacecraft Dataset for Monocular Pose Estimation" v1.0 (DOI: https://doi.org/10.5281/zenodo.6499007) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. These three datasets give the most comprehensive dataset of space borne synthetic images ever published (up to our knowledge).

Overview

The RAD-ChestCT dataset is a large medical imaging dataset developed by Duke MD/PhD student Rachel Draelos during her Computer Science PhD supervised by Lawrence Carin. The full dataset includes 35,747 chest CT scans from 19,661 adult patients. This Zenodo repository contains an initial release of 3,630 chest CT scans, approximately 10% of the dataset. This dataset is of significant interest to the machine learning and medical imaging research communities.

Papers

The following published paper includes a description of how the RAD-ChestCT dataset was created: Draelos et al., "Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes," Medical Image Analysis 2021. DOI: 10.1016/j.media.2020.101857 https://pubmed.ncbi.nlm.nih.gov/33129142/

Two additional papers leveraging the RAD-ChestCT dataset are available as preprints:

"Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks" (https://arxiv.org/abs/2011.08891)

"Explainable multiple abnormality classification of chest CT volumes with deep learning" (https://arxiv.org/abs/2111.12215)

Details about the files included in this data release

Metadata Files (4)

CT_Scan_Metadata_Complete_35747.csv: includes metadata about the whole dataset, with information extracted from DICOM headers.

Extrema_5747.csv: includes coordinates for lung bounding boxes for the whole dataset. Coordinates were derived computationally using a morphological image processing lung segmentation pipeline.

Indications_35747.csv: includes scan indications for the whole dataset. Indications were extracted from the free-text reports.

Summary_3630.csv: includes a listing of the 3,630 scans that are part of this repository.

Label Files (3)

The label files contain abnormality x location labels for the 3,630 shared CT volumes. Each CT volume is annotated with a matrix of 84 abnormality labels x 52 location labels. Labels were extracted from the free text reports using the Sentence Analysis for Radiology Label Extraction (SARLE) framework. For each CT scan, the label matrix has been flattened and the abnormalities and locations are separated by an asterisk in the CSV column headers (e.g. "mass*liver"). The labels can be used as the ground truth when training computer vision classifiers on the CT volumes. Label files include: imgtrain_Abnormality_and_Location_Labels.csv (for the training set)

imgvalid_Abnormality_and_Location_Labels.csv (for the validation set)

imgtest_Abnormality_and_Location_Labels.csv (for the test set)

CT Volume Files (3,630)

Each CT scan is provided as a compressed 3D numpy array (npz format). The CT scans can be read using the Python package numpy, version 1.14.5 and above.

Related Code

Code related to RAD-ChestCT is publicly available on GitHub at https://github.com/rachellea.

Repositories of interest include:

https://github.com/rachellea/ct-net-models contains PyTorch code to load the RAD-ChestCT dataset and train convolutional neural network models for multiple abnormality prediction from whole CT volumes.

https://github.com/rachellea/ct-volume-preprocessing contains an end-to-end Python framework to convert CT scans from DICOM to numpy format. This code was used to prepare the RAD-ChestCT volumes.

https://github.com/rachellea/sarle-labeler contains the Python implementation of the SARLE label extraction framework used to generate the abnormality and location label matrix from the free text reports. SARLE has minimal dependencies and the abnormality and location vocabulary terms can be easily modified to adapt SARLE to different radiologic modalities, abnormalities, and anatomical locations.

Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.

Daniel Buscombe, Marda Science LLC

There are two datasets in this data release:

1. Model training dataset. A manually (or semi-manually) labeled image dataset that was used to train and evaluate a machine (deep) learning model designed to identify subaerial accumulations of large wood, alluvial sediment, water, and vegetation in orthoimagery of alluvial river corridors in forested catchments.

2. Model output dataset. A labeled image dataset that uses the aforementioned model to estimate subaerial accumulations of large wood, alluvial sediment, water, and vegetation in a larger orthoimagery dataset of alluvial river corridors in forested catchments.

All of these label data are derived from raw gridded data that originate from the U.S. Geological Survey (Ritchie et al., 2018). That dataset consists of 14 orthoimages of the Middle Reach (MR, in between the former Aldwell and Mills reservoirs) and 14 corresponding Lower Reach (LR, downstream of the former Mills reservoir) of the Elwha River, Washington, collected between the period 2012-04-07 and 2017-09-22. That orthoimagery was generated using SfM photogrammetry (following Over et al., 2021) using a photographic camera mounted to an aircraft wing. The imagery capture channel change as it evolved under a ~20 Mt sediment pulse initiated by the removal of the two dams. The two reaches are the ~8 km long Middle Reach (MR) and the lower-gradient ~7 km long Lower Reach (LR).

The orthoimagery have been labeled (pixelwise, either manually or by an automated process) according to the following classes (inter class in the label data in parentheses):

1. vegetation / other (0)

2. water (1)

3. sediment (2)

4. large wood (3)

1. Model training dataset.

Imagery was labeled using a combination of the open-source software Doodler (Buscombe et al., 2021; https://github.com/Doodleverse/dash_doodler) and hand-digitization using QGIS at 1:300 scale, rasterizeing the polygons, and gridded and clipped in the same way as all other gridded data. Doodler facilitates relatively labor-free dense multiclass labeling of natural imagery, enabling relatively rapid training dataset creation. The final training dataset consists of 4382 images and corresponding labels, each 1024 x 1024 pixels and representing just over 5% of the total data set. The training data are sampled approximately equally in time and in space among both reaches. All training and validation samples purposefully included all four label classes, to avoid model training and evaluation problems associated with class imbalance (Buscombe and Goldstein, 2022).

Data are provided in geoTIFF format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels.

Pixel-wise labels measurements such as these facilitate development and evaluation of image segmentation, image classification, object-based image-analysis (OBIA), and object-in-image detection models, and numerous potential other machine learning models for the general purposes of river corridor classification, description, enumeration, inventory, and process or state quantification. For example this dataset may serve in transfer learning contexts for application in different river or coastal environments or for different tasks or class ontologies.

Files:

1. Labels_used_for_model_training_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 63 MB, label tiffs

2. Model_training_ images1of4.zip, 1.5 GB, imagery tiffs

3. Model_training_ images2of4.zip, 1.5 GB, imagery tiffs

4. Model_training_ images3of4.zip, 1.7 GB, imagery tiffs

5. Model_training_ images4of4.zip, 1.6 GB, imagery tiffs

2. Model output dataset.

Imagery was labeled using a deep-learning based semantic segmentation model (Buscombe, 2023) trained specifically for the task using the Segmentation Gym (Buscombe and Goldstein, 2022) modeling suite. We use the software package Segmentation Gym (Buscombe and Goldstein, 2022) to fine-tune a Segformer (Xie et al., 2021) deep learning model for semantic image segmentation. We take the instance (i.e. model architecture and trained weights) of the model of Xie et al. (2021), itself fine-tuned on ADE20k dataset (Zhou et al., 2019) at resolution 512x512 pixels, and fine-tune it on our 1024x1024 pixel training data consisting of 4-class label images.

The spatial extent of the imagery in the MR is 455157.2494695878122002,5316532.9804129302501678 : 457076.1244695878122002,5323771.7304129302501678. Imagery width is 15351 pixels and imagery height is 57910 pixels. The spatial extent of the imagery in the LR is 457704.9227139975992031,5326631.3750646486878395 : 459241.6727139975992031,5333311.0000646486878395. Imagery width is 12294 pixels and imagery height is 53437 pixels. Data are provided in Cloud-Optimzed geoTIFF (COG) format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels. All grids have been clipped to the union of extents of active channel margins during the period of interest.

Reach-wide pixel-wise measurements such as these facilitate comparison of wood and sediment storage at any scale or location. These data may be useful for studying the morphodynamics of wood-sediment interactions in other geomorphically complex channels, wood storage in channels, the role of wood in ecosystems and conservation or restoration efforts.

Files:

1. Elwha_MR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 9.67 MB, label COGs from Elwha River Middle Reach (MR)

2. ElwhaMR_ imagery_ part1_ of 2.zip, 566 MB, imagery COGs from Elwha River Middle Reach (MR)

3. ElwhaMR imagery_ part2_ of_ 2.zip, 618 MB, imagery COGs from Elwha River Middle Reach (MR)

3. Elwha_LR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 10.96 MB, label COGs from Elwha River Lower Reach (LR)

4. ElwhaLR_ imagery_ part1_ of 2.zip, 622 MB, imagery COGs from Elwha River Middle Reach (MR)

5. ElwhaLR imagery_ part2_ of_ 2.zip, 617 MB, imagery COGs from Elwha River Middle Reach (MR)

This dataset was created using open-source tools of the Doodleverse, a software ecosystem for geoscientific image segmentation, by Daniel Buscombe (https://github.com/dbuscombe-usgs) and Evan Goldstein (https://github.com/ebgoldstein). Thanks to the contributors of the Doodleverse!. Thanks especially Sharon Fitzpatrick (https://github.com/2320sharon) and Jaycee Favela for contributing labels.

References

• Buscombe, D. (2023). Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8172858

• Buscombe, D., Goldstein, E. B., Sherwood, C. R., Bodine, C., Brown, J. A., Favela, J., et al. (2021). Human-in-the-loop segmentation of Earth surface imagery. Earth and Space Science, 9, e2021EA002085. https://doi.org/10.1029/2021EA002085

• Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

• Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., https://doi.org/10.3133/ofr20211039.

• Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC.

• Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P., 2021. SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, pp.12077-12090.

• Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A. and Torralba, A., 2019. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127, pp.302-321.

Total-Text is a dataset tailored for instance segmentation, semantic segmentation, and object detection tasks, containing 1555 images with 11165 labeled objects belonging to a single class — text with text label tag. Its primary aim is to open new research avenues in the scene text domain. Unlike traditional text datasets, Total-Text uniquely includes curved-oriented text in addition to horizontal and multi-oriented text, offering diverse text orientations in more than half of its images. This variety makes it a crucial resource for advancing text-related studies in computer vision and natural language processing.

1. Overview This dataset is a collection of 6,000+ images of mixed race human face with various expressions & emotions that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. The data set This dataset contains 6,000+ images of face emotion. Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ or contact via our email contact@pixta.ai."

Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model’s accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing.

Virtual KITTI is a photo-realistic synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation, optical flow, and depth estimation. Virtual KITTI contains 21,260 images generated from five different virtual worlds in urban settings under different imaging and weather conditions. These photo-realistic synthetic images are automatically, exactly, and fully annotated for 2D and 3D multi-object tracking and at the pixel level with category, instance, flow, and depth labels.

Nasal Cytology, or Rhinology, is the subfield of otolaryngology, focused on the microscope observation of samples of the nasal mucosa, aimed to recognize cells of different types, to spot and diagnose ongoing pathologies. Such methodology can claim good accuracy in diagnosing rhinitis and infections, being very cheap and accessible without any instrument more complex than a microscope, even optical ones. Mucosa samples are taken non-invasively, just using a simple swab, to be then smeared onto a glass (fixation) and coloured with staining (in the case of the NMCD dataset the May-Grunwald-Giemsa) before being observed at the microscope.The construction of the NCD dataset is the result of intense work and collaboration between otolaryngologists and computer scientists who, convinced of the great contribution that artificial intelligence can make to this branch of medicine, decided to make material available to the scientific community to allow them to challenge and confront each other in this new application field. In this dataset 10 different entities are identified, that are distinguishable by some specific characteristic:Epithelial Cells: main components of nasal mucosa, constituting 80% of the observed cytotype in health patients. Their presence is not associated with ongoing pathologies.Ciliated cells: belonging to the epithelium cells family, these cells are characterized by their ”tailed-like” shape.Metaplastic cells: also belonging to the epithelium cells family, mataplastic cells are characterized by their round shape. Their presence is usually associated with ongoing inflammatory reaction.Muciparous: calciform mucous-secreting cells characterized by a bilobed shape with chromatin reinforced membrane. The increase of muciparous cells results in increased mucus production, a symptom of nasal pathologies with chronic trends, like, in example, dust mites allergies.Neutrophils: granulocytes with several nucleoli and a round shape. Their main function is the phagocytosis of germs. An increase in their number should always be kept under control as an immune response indicator.Eosinophils: polynuclear granulocytes, slightly large than neutrophils. The MGG staining tends to highlight the eosinophil grains within them in an orange color. Allergic diseases are associated with an increase in their population.Lymphocytes: white blood cells responsible for the immune response. Their large nucleus is surrounded by a thin cytoplasmatic ”light blue” rim.Mast-cells: large oval cells having their nuclei covered with basophil granules of intense color. Their presence in the nasal mucosa is caused by ongoing allergies.Ematia (Erythrocyte): red blood cells whose occurrence in rhinological specimen may be due to pathologies or previous internal nose wounds, or even to small blood losses during the smear process.Artifacts: with this name, are classified all objects with morphology similar to the one of a cell but not being onet. Examples of artifacts may be pollen pieces or dirt spots on the slide. Data were sampled from 14 rhinological slides collected at the Rhinology Clinic of the Otolaryngology Department of the University of Bari. Collecting technique was the direct smear and staining was the MGG. An optical microscope ProWay XSZPW208T with 1000x zoom, equipped with a 3MP DCE-PW300 camera was used to acquire 50 images (microscope fields) from each slide: this specific quantity has been chosen since it is the one defined in the rhino cytology protocol.Thus 700 images with a size of 1024×768 were obtained. The image annotations were created by experts, using the Roboflow platform, analyzing each image individually, annotating and labeling each cell. During such phase, a dropping policy was followed, discarding images where were detected:sampling noise (i.e. dirt on the slide or blurred photos)duplication of large cytoplasmic areas already present in other imagestoo dense and confused clusters of cells, typically discarded by nasal cytologist.A total of 200 cytological fields were pruned, ending up with 500 images. A Bounding Box (BB) was manually drawn on each cell in the images, to which a label was attached to specify the class the cell belonged to. Being cells generally round, the smallest rectangular area that enclose them was marked as their bounding box.It is hence possible to find overlaps between BBs in images, owed by the proximity between the cells and the rectangular structure of the box. Labeling operations produced more than 10,000 BBs corresponding to cells. Thanks to Roboflow, annotations were made available in any standard annotation format required for computer vision algorithms, like Pascal Voc, Coco, Tensorflow and Yolo. The 500 microscopic fields images were divided into training, validation and test set (80%-10%-10%) using the stratified holdout strategy to maintain the same class distribution within the three sets.

Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the utility of machine learning (ML) models learned from them. Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long, captured in natural lighting conditions, and annotated with eleven visually perceptible behaviors of grazing cattle. By creating and sharing CVB, our aim is to develop improved models capable of recognizing all important behaviors accurately and to assist other researchers and practitioners in developing and evaluating new ML models for cattle behavior classification using video data. The dataset is presented in the form of following three sub-directories. 1. raw_frames: contains 450 frames in each sub folder, representing 15 sec video, taking at a frames rate of 30 FPS, 2. annotations: contains the json files corresponding to the raw_frames folder. We have one json file for one video, containing the bounding box annotations for each cattle and their associated behaviors, and 3. CVB_in_AVA_format: contains the CVB data in the standard AVA dataset format which we have used to apply SlowFast model. Lineage: We use the Computer Vision Annotation Tool (CVAT) to collect our annotations. To make the procedure more efficient, we perform an initial detection and tracking of cattle in the videos using appropriate pre-trained models. The results are corrected by domain experts along with cattle behavior labeling in CVAT. The pre-hoc detection and tracking step significantly reduces the manual annotation time and effort.

A dataset of 70,000 fashion images with labels for 10 classes. The dataset was created by researchers at Zalando Research and is used for research in machine learning and computer vision tasks such as image classification.

The Brazilian Multilabel Ophthalmological Dataset (BRSET) is a multi-labeled ophthalmological dataset designed to improve scientific community development and validate machine learning models. In ophthalmology, ancillary exams support medical decisions and can be used to develop algorithms; however, the availability and representativeness of ophthalmological datasets are limited. This dataset consists of 16,266 images from 8,524 Brazilian patients. Demographics, macula, optic disc, and vessels anatomical parameters, focus, illumination, image field, and artifacts as quality control, and multi-labels are included alongside color fundus retinal photos. This dataset enables computer vision models to predict demographic characteristics and multi-label disease classification using retinal fundus photos.

In this dataset, we present a set of angiographic imaging series of one hundred patients who underwent coronary angiography using Coroscop (Siemens) and Innova (GE Healthcare) image-guided surgery systems at the Research Institute for Complex Problems of Cardiovascular Diseases (Kemerovo, Russia). All patients had angiographically and/or functionally confirmed one-vessel coronary artery disease (≥70% diameter stenosis by quantitative coronary analysis or 50 - 69% with FFR (fractional flow reserve) ≤ 0.80 or stress echocardiography evidence of regional ischemia). For the purpose of our study, significant coronary stenosis was defined according to 2017 US appropriate use criteria for coronary revascularization in patients with stable ischemic heart disease. The study design was approved by the Local Ethics Committee of the Research Institute for Complex Issues of Cardiovascular Diseases (approval letter No. 112 issued on May 11, 2018). All participants provided written informed consent to participate in the study. Coronary angiography was performed by the single operator according to the indications and recommendations stated in the 2018 ESC/EACTS Guidelines on myocardial revascularization. The presence or absence of coronary stenosis was confirmed by the same operator using angiography imaging series according to the 2018 ESC/EACTS Guidelines on myocardial revascularization.

Angiographic images of the radiopaque overlaid coronary arteries with stenotic segments were selected and then converted into separate images. An interventional cardiologist rejected non-informative images and selected only those containing contrast passages through a stenotic vessel. A total of 8325 grayscale images (100 patients) of 512×512 to 1000×1000 pixels were included in the dataset. Data were labeled using the LabelBox, a free version of SaaS (Software as a Service).

We additionally estimated the size of the stenotic region by computing the area of the bounding box. Similar to the Common Objects in Context dataset, we divided objects by their area into three types: small (area < 322), medium (322 ≤ area ≤ 962), and large (area > 962). 2509 small objects (30%), 5704 medium objects (69%), and 113 large objects (1%) were obtained in the input data.

Context

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does work on MNIST, it may still fail on others."

Zalando seeks to replace the original MNIST dataset

Content

Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. The training and test data sets have 785 columns. The first column consists of the class labels (see above), and represents the article of clothing. The rest of the columns contain the pixel-values of the associated image.

To locate a pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27. The pixel is located on row i and column j of a 28 x 28 matrix. For example, pixel31 indicates the pixel that is in the fourth column from the left, and the second row from the top, as in the ascii-diagram below.

Labels

Each training and test example is assigned to one of the following labels:

0 T-shirt/top 1 Trouser 2 Pullover 3 Dress 4 Coat 5 Sandal 6 Shirt 7 Sneaker 8 Bag 9 Ankle boot

TL;DR

Each row is a separate image Column 1 is the class label. Remaining columns are pixel numbers (784 total). Each value is the darkness of the pixel (1 to 255) Acknowledgements

Original dataset was downloaded from https://github.com/zalandoresearch/fashion-mnist Dataset was converted to CSV with this script: https://pjreddie.com/projects/mnist-in-csv/ License

The MIT License (MIT) Copyright © [2017] Zalando SE, https://tech.zalando.com

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