Introduction The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor Data To download the training/validation data, see the development kit. The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image. Some example images can be viewed online. A subset of images are also annotated with pixel-wise segmentation of each object presen

The PASCAL Visual Object Classes Challenge (VOC) is a benchmark dataset for object detection and semantic segmentation.

Dataset

This dataset was created by k201669 Syed Jafri

Contents

The 2016 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify heart sound recordings collected from a variety of clinical or nonclinical (such as in-home visits) environments. The aim is to identify, from a single short recording (10-60s) from a single precordial location, whether the subject of the recording should be referred on for an expert diagnosis.

Data To download the training/validata data, see the development kit. In total there are 10,057 images [further statistics]. The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image. Some example images can be viewed online. Annotation was performed according to a set of guidelines distributed to all annotators. The data will be made available in two stages; in the first stage, a development kit will be released consisting of training and validation data, plus evaluation software (written in MATLAB). One purpose of the validation set is to demonstrate how the evaluation software works ahead of the competition submission. In the second stage, the test set will be made available for the actual competition. As in the VOC2007 challenge, no ground truth for the test d

The Pascal Visual Object Classes (VOC) Challenge has been an annual event since 2006. The challenge consists of two components: (i) a publicly available dataset of images obtained from the Flickr web site, together with ground truth annotation and standardised evaluation software; and (ii) an annual competition and workshop. The most popular part of the dataset is segmentation, which is presented on the DatasetNinja.

Details of the contributor of each image can be found in the file "contrib.txt" included in the database. Categories Views of bicycles, buses, cats, cars, cows, dogs, horses, motorbikes, people, sheep in arbitrary pose. Number of images 5,304 Number of annotated images 5,304

Dataset

This dataset was created by Christopher Bovolos

Contents

A benchmark for object detection

The PASCAL VOC project:

• Provides standardised image data sets for object class recognition
• Provides a common set of tools for accessing the data sets and annotations
• Enables evaluation and comparison of different methods
• Ran challenges evaluating performance on object class recognition (from 2005-2012, now finished)

Context

The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are:

• Person: person
• Animal: bird, cat, cow, dog, horse, sheep
• Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train
• Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor
• There will be two main competitions, and two smaller scale "taster" competitions.

Content

The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image.

Acknowledgements

@misc{pascal-voc-2007, author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.", title = "The {PASCAL} {V}isual {O}bject {C}lasses {C}hallenge 2007 {(VOC2007)} {R}esults", howpublished = "http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html"}

The authors of the PASCAL Context dataset conduct a comprehensive investigation into the significance of context within existing state-of-the-art detection and segmentation methodologies. Their approach involves the meticulous labeling of every pixel encompassed within the PASCAL VOC 2010 detection challenge, associating each pixel with a semantic category. This dataset is envisioned to present a considerable challenge to the research community, as it incorporates an impressive 520 additional classes that cater to both semantic segmentation and object detection.

Context

This dataset was originally for a machine learning challenge to classify heart beat sounds. The data was gathered from two sources: (A) from the general public via the iStethoscope Pro iPhone app, and (B) from a clinic trial in hospitals using the digital stethoscope DigiScope. There were two challenges associated with this competition:

1. Heart Sound Segmentation
The first challenge is to produce a method that can locate S1(lub) and S2(dub) sounds within audio data, segmenting the Normal audio files in both datasets.

2. Heart Sound Classification
The task is to produce a method that can classify real heart audio (also known as “beat classification”) into one of four categories.

Content

The dataset is split into two sources, A and B:

set_a.csv - Labels and metadata for heart beats collected from the general public via an iPhone app

set_a_timing.csv - contains gold-standard timing information for the "normal" recordings from Set A.

set_b.csv - Labels and metadata for heart beats collected from a clinical trial in hospitals using a digital stethoscope

audio files - Varying lengths, between 1 second and 30 seconds. (some have been clipped to reduce excessive noise and provide the salient fragment of the sound).

Acknowledgements

    author = "Bentley, P. and Nordehn, G. and Coimbra, M. and Mannor, S.",
    title = "The {PASCAL} {C}lassifying {H}eart {S}ounds {C}hallenge 2011 {(CHSC2011)} {R}esults",
    howpublished = "http://www.peterjbentley.com/heartchallenge/index.html"} ```

## Inspiration

Try your hand at automatically separating normal heartbeats from abnormal heartbeats and heart murmur with this machine learning challenge by [Peter Bentley et al](http://www.peterjbentley.com/heartchallenge/)

The goal of the task was to first (1) identify the locations of heart sounds from the audio, and (2) to classify the heart sounds into one of several categories (normal v. various non-normal heartbeat sounds).

The Pascal VOC 2012 test set consists of 10,000 images. The images in the test set are a challenging dataset, with a wide range of object classes. The images in the test set are also not labeled, so they can be used to evaluate the performance of object detection algorithms.

PASCAL VOC 2011 COMPETITIONS

The main challenges have run each year since 2005. For more background on VOC, the following journal paper discusses some of the choices we made and our experience in running the challenge, and gives a more in depth discussion of the 2007 methods and results:

The PASCAL Visual Object Classes (VOC) Challenge Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J. and Zisserman, A. International Journal of Computer Vision, 88(2), 303-338, 2010

Content

20 classes + 3 (head,foot,hand)

aeroplane bicycle bird boat bottle bus car cat chair cow diningtable dog horse motorbike person pottedplant sheep sofa train tvmonitor

Acknowledgements

We gratefully acknowledge the following, who spent many long hours providing annotation for the VOC2011 database:

Yusuf Aytar, Lucia Ballerini, Hakan Bilen, Ken Chatfield, Mircea Cimpoi, Ali Eslami, Basura Fernando, Christoph Godau, Bertan Gunyel, Phoenix/Xuan Huang, Jyri Kivinen, Markus Mathias, Kristof Overdulve, Konstantinos Rematas, Johan Van Rompay, Gilad Sharir, Mathias Vercruysse, Vibhav Vineet, Ziming Zhang, Shuai Kyle Zheng.

We also thank Yusuf Aytar for continued development and administration of the evaluation server, and Ali Eslami for analysis of the results.

The PASCAL Visual Object Classes Challenge 2012 (VOC2012) VOCtestnoimgs_06-Nov-2007.tar

This package contains supplementary material for our article prepared for publication and under revision. It contains omitted results due to space limits of the article as well as detailed, patient per patient and team per team results for all metrics. Additional figures redundant with those of the article are also provided.

The readme file Readme_SupplementalMaterial.txt provides details about each individual file content.

Dataset

This dataset was created by Timothée Pascal

Contents

Snapshot of Pascal tool (http://www2.unil.ch/cbg/index.php?title=Pascal) used for the challenge and scripts to compute the scores. Website: https://synapse.org/modulechallenge. Preprint: Choobdar, S., Ahsen, M.E., Crawford, J., et al. (2018). Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases. bioRxiv 265553. https://www.biorxiv.org/content/early/2018/02/15/265553

Dataset

This dataset was created by Pascal Pfeiffer

Released under Data files © Original Authors

Contents

The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor There will be three main competitions: classification, detection, and segmentation; and three "taster" competition: person layout, action classification, and ImageNet large scale recognition: Classification/Detection Competitions Classification: For each of the twenty classes, predicting presence/absence of an example of that class in the test image.