The CryoPPP dataset consists of 34 ground truth data and metadata for 335 EMPIAR IDs. The ground truth data is comprised of a variety of 9893 Micrographs (~300 cryo-EM images per EMPIAR ID) with manually curated ground truth coordinates of picked protein particles. The metadata consists of 1,698,802 high-resolution micrographs deposited in EMPIAR with their respective FPT and Globus data download paths.
1 PAPER • NO BENCHMARKS YET
The BCSS dataset contains over 20,000 segmentation annotations of tissue regions from breast cancer images from The Cancer Genome Atlas (TCGA). This large-scale dataset was annotated through the collaborative effort of pathologists, pathology residents, and medical students using the Digital Slide Archive. It enables the generation of highly accurate machine-learning models for tissue segmentation.
0 PAPER • NO BENCHMARKS YET
This dataset is an extremely challenging set of over 20,000+ original Construction vehicle images captured and crowdsourced from over 600+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs.
This dataset is the images of corn seeds considering the top and bottom view independently (two images for one corn seed: top and bottom). There are four classes of the corn seed (Broken-B, Discolored-D, Silkcut-S, and Pure-P) 17802 images are labeled by the experts at the AdTech Corp. and 26K images were unlabeled out of which 9k images were labeled using the Active Learning (BatchBALD)
Context As mentioned in the reference paper:
FAscicle Lower Leg Muscle Ultrasound Dataset is a dataset composed of 812 ultrasound images of lower leg muscles to analyze muscle weaknesses and prevent injuries. It combines the datasets provided by two articles, “Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks” published by Ryan Cunningham et al. and “Automated Analysis of Musculoskeletal Ultrasound Images Using Deep Learning” published by Neil Cronin, with complementary annotations. The dataset has been introduced in this paper: Michard, H., Luvison, B., Pham, Q. C., Morales-Artacho, A. J., & Guilhem, G. (2021, August). AW-Net: automatic muscle structure analysis on B-mode ultrasound images for injury prevention. In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 1-9).
InfiniteRep is a synthetic, open-source dataset for fitness and physical therapy (PT) applications. It includes 1k videos of diverse avatars performing multiple repetitions of common exercises. It includes significant variation in the environment, lighting conditions, avatar demographics, and movement trajectories. From cadence to kinematic trajectory, each rep is done slightly differently -- just like real humans. InfiniteRep videos are accompanied by a rich set of pixel-perfect labels and annotations, including frame-specific repetition counts.
This dataset were acquired with the Airphen (Hyphen, Avignon, France) six-band multi-spectral camera configured using the 450/570/675/710/730/850 nm bands with a 10 nm FWHM. And acquired on the site of INRAe in Montoldre (Allier, France, at 46°20'30.3"N 3°26'03.6"E) within the framework of the “RoSE challenge” founded by the French National Research Agency (ANR). Images contains bean, with various natural weeds (yarrows, amaranth, geranium, plantago, etc) and sowed ones (mustards, goosefoots, mayweed and ryegrass) with very distinct characteristics in terms of illumination (shadow, morning, evening, full sun, cloudy, rain, ...) The ground truth is defined for each images with polygons around leafs boundaries: In addition, each polygons are labeled into crop or weed. (2020-06-11)
The dataset is organized into 24 typical scenarios, showcasing the richness of real-world environments, conditions, and objects. It is carefully curated to reflect diverse and realistic situations, allowing models to be tested and refined under a wide range of conditions.