Omkar2118/datasets huggingface dataset:
ConductorMotion100

We construct a large-scale conducting motion dataset, named ConductorMotion100, by deploying pose estimation on conductor view videos of concert performance recordings collected from online video platforms. The construction of ConductorMotion100 removes the need for expensive motion-capture equipment and makes full use of massive online video resources. As a result, the scale of ConductorMotion100 has reached an unprecedented length of 100 hours.
Azure Functions Trace 2019
Full name: Azure Functions Trace 2019

This is a set of files representing part of the workload of Microsoft's Azure Functions offering, collected in July of 2019. This dataset is a subset of the data described in, and analyzed, in the USENIX ATC 2020 paper 'Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider'.

Functions in Azure Functions are grouped into Applications. Included here is only data pertaining to a random sample of Azure Functions applications. The sampling is done per application, so that if there is data about an application in the trace, then all of its functions are included. The sampling rate is small and unspecified, but as the accompanying notebook shows, the distributions in the released trace are a good match to those in the ATC paper.

In Azure Functions, applications are the unit of resource allocation. This has a few practical implications: for example, warm-up decisions are made at the application level, and memory allocation is measured by application, not by function. The 'HashOwner' field in these files is used to group applications that belong to the same subscription in Azure. It is included to indicate applications that are possibly related to each other.

The dataset comprises this description, and an R notebook with plots comparing the released trace with the ATC paper, and the following sets of files:

    Function invocation counts and triggers
    Function execution time distributions
    Application memory allocation distributions

Introducing paper: Microsoft Research Asia's Systems for WMT19
MuCeD

MuCeD, a dataset that is carefully curated and validated by expert pathologists  from the All India Institute of Medical Science (AIIMS), Delhi, India. The H&E-stained histopathology images of the human duodenum in MuCeD are captured through an Olympus BX50 microscope at 20x zoom using a DP26 camera with each image being  1920x2148 in dimension. The dataset has 55 images, with bounding boxes for 2,090 IELs and 6,518 ENs annotated using the LabelMe software and are further validated by multiple pathologists. These cells are selected from the epithelial area -- a region of interest that has been explicitly segmented by experts. The epithelial area denotes the area of continuous villi and is used for cell detection, whereas rest of the area is masked out. Further, each image is sliced into 9 subimages and each subimage is re-scaled to 640x640, before it is given as input to object detection models. We divide 55 images into five folds of 11 images each and report 5-fold crossvalidation numbers. Within 44 training images in a given fold, 8 are used for validation and 36 for training.  
Data is annotated in yolo format with labels are present in .txt files with class x, y, width,  heigh format.

Introducing paper: DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting