datasets.js

import React from 'react';
import * as c from './constants.js';



export const DATASET_DATA = [ 
    {
        "title": "CamVid",
        "type": c.DATASET,
        "description": `The Cambridge-driving Labeled Video Database (CamVid) is the first collection of videos with object 
                        class semantic labels, complete with metadata. The database provides ground truth labels that associate
                        each pixel with one of 32 semantic classes.`,
        "topics": [c.SELF_DRIVING, c.CARS],
        "problem_types": [c.SEGMENTATION, c.VIDEO],
        "website_url": "http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/",
        "paper_url": "http://www.cs.ucl.ac.uk/staff/G.Brostow/papers/SemanticObjectClassesInVideo_BrostowEtAl2009.pdf"
    },
    {
        "title": "MNIST",
        "type": c.DATASET,
        "description": `The MNIST database of handwritten digits, available from this page, has a training set of 60,000 
                        examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. 
                        The digits have been size-normalized and centered in a fixed-size image.`,
        "topics": [],
        "problem_types": [c.CLASSIFICATION],
        "website_url": "http://yann.lecun.com/exdb/mnist/",
        "paper_url": null
    },
    {
        "title": "Cifar-10",
        "type": c.DATASET,
        "description": `The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. 
                        There are 50000 training images and 10000 test images.`,
        "topics": [],
        "problem_types": [c.CLASSIFICATION],
        "website_url": "https://www.cs.toronto.edu/~kriz/cifar.html",
        "paper_url": null
    },
    {
        "title": "Cifar-100",
        "type": c.DATASET,
        "description": `This dataset is just like CIFAR-10, except it has 100 classes containing 600 images each. There are 
                        500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped 
                        into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a 
                        "coarse" label (the superclass to which it belongs).`,
        "topics": [],
        "problem_types": [c.CLASSIFICATION],
        "website_url": "https://www.cs.toronto.edu/~kriz/cifar.html",
            "paper_url": null
    },
    {
        "title": "MS COCO",
        "type": c.DATASET,
        "description": `COCO is Microsoft's large-scale object detection, segmentation, and captioning dataset. Includes class labels, 
                        bounding boxes, keypoints, captions, and instance segmentation annotations.`,
        "topics": [c.GENERAL],
        "problem_types": [c.CLASSIFICATION, c.OBJECT_DETECTION, c.SEGMENTATION, c.KEYPOINT, c.CAPTIONING],
        "website_url":"http://mscoco.org/dataset/#overview",
        "paper_url": "http://arxiv.org/abs/1405.0312"
    },
    {
        "title": "ImageNet",
        "type": c.DATASET,
        "description": `Image database organized according to the WordNet hierarchy in which each node 
                        of the hierarchy is depicted by hundreds and thousands of images.`,
        "topics": [c.GENERAL],
        "problem_types": [c.CLASSIFICATION, c.OBJECT_DETECTION],
        "website_url":"http://image-net.org/index",
        "paper_url": null
    },
    {
        "title": "Pascal VOC",
        "type": c.DATASET,
        "description": `20 classes. The train/val data has 11,530 images containing 27,450 ROI annotated objects and 6,929 segmentations.`,
        "topics": [c.GENERAL],
        "problem_types": [c.CLASSIFICATION, c.OBJECT_DETECTION, c.SEGMENTATION],
        "website_url":"http://host.robots.ox.ac.uk/pascal/VOC/",
        "paper_url": "http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.pdf"
    },
    {
        "title": "Places205",
        "type": c.DATASET,
        "description": `Scene recognition is one of the hallmark tasks of computer vision, allowing defining a context for object 
                        recognition. Here we introduce a new scene-centric database called Places, with 205 scene categories 
                        and 2.5 millions of images with a category label.`,
        "topics": [c.NATURAL_SCENES],
        "problem_types": [c.CLASSIFICATION],
        "website_url":"http://places.csail.mit.edu/downloadData.html",
        "paper_url": "http://places.csail.mit.edu/places_NIPS14.pdf"
    },
    { 
        "title":"10k US Adult Faces Database",
        "type": c.DATASET,
        "description": `This database contains 10,168 natural face photographs and several measures for 2,222 of the faces, 
                        including memorability scores, computer vision and psychology attributes, and landmark point annotations.
                        The face photographs are JPEGs with 72 pixels/in resolution and 256-pixel height.`,
        "topics": [c.PSYCHOLOGY, c.FACE_DETECTION],
        "problem_types": [c.LANDMARK, c.SENTIMENT, c.FACE_DETECTION],
        "website_url":  "http://wilmabainbridge.com/facememorability2.html",
        "paper_url": "http://wilmabainbridge.com/papers/jepg-2013.pdf",
    },
    { 
        "title": "Cats vs Dogs",
        "type": c.DATASET,
        "description": "From the Kaggle Cats vs Dogs competition. The training archive contains 25,000 images of dogs and cats.",
        "topics": [c.ANIMALS],
        "problem_types": [c.CLASSIFICATION, c.COMPUTER_VISION],
        "website_url":  "https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data",
        "paper_url": null,
    },
    { 
        "title": "Planet Amazon Rainforest",
        "type": c.DATASET,
        "description": `From the Kaggle Planet Amazon Rainforest competition. The training archive contains 100,000 satellite images 
                        of the Amazon rainforest of which 40K are annotated with one or more labels. Includes both JPG and TIF formats.`,
        "topics": [c.SATELLITE],
        "problem_types": [c.CLASSIFICATION, c.MULTILABEL, c.COMPUTER_VISION],
        "website_url":  "https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data",
        "paper_url": null,
    },
    { 
        "title": "Audience Unfiltered faces for gender and age classification",
        "type": c.DATASET,
        "description": `In order to facilitate the study of age and gender recognition, we provide a data set and benchmark of face photos. 
                        The data included in this collection is intended to be as true as possible to the challenges of real-world imaging conditions. 
                        In particular, it attempts to capture all the variations in appearance, noise, pose, lighting and more, that can be expected 
                        of images taken without careful preparation or posing.`,
        "topics": [c.FACES],
        "problem_types": [c.COMPUTER_VISION, c.CLASSIFICATION],
        "website_url":  "http://www.openu.ac.il/home/hassner/Adience/data.html",
        "paper_url": null,
     },
    { 
        "title": "Affective Image Classification",
        "type": c.DATASET,
        "description": `The International Affective Picture System (IAPS) is being developed to provide a set of normative emotional stimuli 
                        for experimental investigations of emotion and attention. The goal is to develop a large set of standardized, emotionally-evocative, 
                        internationally-accessible, color photographs that includes contents across a wide range of semantic categories.`,
        "topics": [c.PSYCHOLOGY],
        "problem_types": [c.COMPUTER_VISION, c.CLASSIFICATION, c.SENTIMENT],
        "website_url":  "http://csea.phhp.ufl.edu/media.html",
        "paper_url": "http://www.imageemotion.org/machajdik_hanbury_affective_image_classification.pdf",
    },
    { 
        "title": "Animals with attributes",
        "type": c.DATASET,
        "description": `This dataset provides a plattform to benchmark transfer-learning algorithms, in particular attribute base classification. 
                        It consists of 30475 images of 50 animals classes with six pre-extracted feature representations for each image. The animals classes 
                        are aligned with Osherson's classical class/attribute matrix, thereby providing 85 numeric attribute values for each class. 
                        Using the shared attributes, it is possible to transfer information between different classes.`,
        "topics": [c.ANIMALS],
        "problem_types": [c.COMPUTER_VISION, c.CLASSIFICATION],
        "website_url":  "https://cvml.ist.ac.at/AwA/",
        "paper_url": "http://cvml.ist.ac.at/papers/lampert-cvpr2009.pdf",
    },
    { 
        "title": "Caltech Pedestrian Detection Benchmark",
        "type": c.DATASET,
        "description": "",
        "topics": [],
        "problem_types": [],
        "website_url":  "https://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/",
        "paper_url": null,
    },
    { 
        "title": "Chars74K dataset, Character Recognition in Natural Images (both English and Kannada are available)",
        "type": c.DATASET,
        "description": "",
        "topics": [],
        "problem_types": [c.NLP, c.OCR],
        "website_url":  "http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/",
        "paper_url": null,
    },
    { 
        "title": "Face Recognition Benchmark",
        "type": c.DATASET,
        "description": "",
        "topics": [c.FACE_DETECTION, c.FACES],
        "problem_types": [],
        "website_url":  "http://www.face-rec.org/databases/",
        "paper_url": null,
    },
    { 
        "title": "GDXray:  X-ray images for X-ray testing and Computer Vision",
        "type": c.DATASET,
        "description": "",
        "topics": [],
        "problem_types": [],
        "website_url":  "http://dmery.ing.puc.cl/index.php/material/gdxray/",
        "paper_url": null,
    },
    { 
        "title": "ImageNet (in WordNet hierarchy)",
        "type": c.DATASET,
        "description": "",
        "topics": [],
        "problem_types": [],
        "website_url":  "http://www.image-net.org/",
        "paper_url": null,
    },
    { 
        "title": "Indoor Scene Recognition",
        "type": c.DATASET,
        "description": "",
        "topics": [],
        "problem_types":[],
        "website_url": "http://web.mit.edu/torralba/www/indoor.html",
        "paper_url": null,
    },
    { 
        "title":"International Affective Picture System, UFL",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://csea.phhp.ufl.edu/media/iapsmessage.html",
        "paper_url": null,
    },
    { 
        "title":"Massive Visual Memory Stimuli, MIT",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://cvcl.mit.edu/MM/stimuli.html",
        "paper_url": null,
    },
    { 
        "title":"MNIST database of handwritten digits, near 1 million examples",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://yann.lecun.com/exdb/mnist/",
        "paper_url": null,
    },
    { 
        "title":"Several Shape-from-Silhouette Datasets",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://kaiwolf.no-ip.org/3d-model-repository.html",
        "paper_url": null,
    },
    { 
        "title":"Stanford Dogs Dataset",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://vision.stanford.edu/aditya86/ImageNetDogs/",
        "paper_url": null,
    },
    { 
        "title":"SUN database, MIT",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://groups.csail.mit.edu/vision/SUN/hierarchy.html",
        "paper_url": null,
    },
    { 
        "title":"The Action Similarity Labeling (ASLAN) Challenge",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://www.openu.ac.il/home/hassner/data/ASLAN/ASLAN.html",
        "paper_url": null,
    },
    { 
        "title":"The Oxford-IIIT Pet Dataset",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://www.robots.ox.ac.uk/~vgg/data/pets/",
        "paper_url": null,
    },
    { 
        "title":"Violent-Flows - Crowd Violence \ Non-violence Database and benchmark",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://www.openu.ac.il/home/hassner/data/violentflows/",
        "paper_url": null,
    },
    { 
        "title":"Visual genome",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://visualgenome.org/api/v0/api_home.html",
        "paper_url": null,
    },
    { 
        "title":"YouTube Faces Database",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://www.cs.tau.ac.il/~wolf/ytfaces/",
        "paper_url": null,
    },
    { 
        "title":"Context-aware data sets from five domains",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "https://github.com/irecsys/CARSKit/tree/master/context-aware_data_sets",
        "paper_url": null,
    },
    { 
        "title":"Delve Datasets for classification and regression (Univ. of Toronto)",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://www.cs.toronto.edu/~delve/data/datasets.html",
        "paper_url": null,
    },
    { 
        "title":"Discogs Monthly Data",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://data.discogs.com/",
        "paper_url": null,
    },
    { 
        "title":"eBay Online Auctions (2012)",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://www.modelingonlineauctions.com/datasets",
        "paper_url": null,
    },
    { 
        "title":"IMDb Database",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://www.imdb.com/interfaces",
        "paper_url": null,
    },
    { 
        "title":"Keel Repository for classification, regression and time series",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://sci2s.ugr.es/keel/datasets.php",
        "paper_url": null,
    },
    { 
        "title":"Labeled Faces in the Wild (LFW)",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://vis-www.cs.umass.edu/lfw/",
        "paper_url": null,
    },
    { 
        "title":"Lending Club Loan Data",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "https://www.lendingclub.com/info/download-data.action",
        "paper_url": null,
    },
    { 
        "title":"Machine Learning Data Set Repository",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://mldata.org/",
        "paper_url": null,
    },
    { 
        "title":"Million Song Dataset",
        "type": c.DATASET,
        "description":"",
        "topics":[c.MUSIC],
        "problem_types":[],
        "website_url": "http://labrosa.ee.columbia.edu/millionsong/",
        "paper_url": null,
    },
    { 
        "title":"More Song Datasets",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[c.MUSIC],
        "website_url": "http://labrosa.ee.columbia.edu/millionsong/pages/additional-datasets",
        "paper_url": null,
    },
    { 
        "title":"MovieLens Data Sets",
        "type": c.DATASET,
        "description":"",
        "topics":[c.MOVIES],
        "problem_types":[],
        "website_url": "http://grouplens.org/datasets/movielens/",
        "paper_url": null,
    },
    { 
        "title":"New Yorker caption contest ratings",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[c.CAPTIONING],
        "website_url": "https://github.com/nextml/caption-contest-data",
        "paper_url": null,
    },
    { 
        "title":"Registered Meteorites on Earth",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[c.SPACE],
        "website_url": "http://healthintelligence.drupalgardens.com/content/registered-meteorites-has-impacted-earth-visualized",
        "paper_url": null,
    },
    { 
        "title":"Restaurants Health Score Data in San Francisco",
        "type": c.DATASET,
        "description":"",
        "topics":[c.RESTAURANTS],
        "problem_types":[],
        "website_url": "http://missionlocal.org/san-francisco-restaurant-health-inspections/",
        "paper_url": null,
    },
    { 
        "title":"UCI Machine Learning Repository",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://archive.ics.uci.edu/ml/",
        "paper_url": null,
    },
    { 
        "title": "Yahoo! Ratings and Classification Data",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[],
        "website_url": "http://webscope.sandbox.yahoo.com/catalog.php?datatype=r",
        "paper_url": null,
    },
    { 
        "title": "Youtube 8m",
        "type": c.DATASET,
        "description":`YouTube-8M is a large-scale labeled video dataset that consists of millions of 
                       YouTube video IDs and associated labels from a diverse vocabulary of 4700+ visual entities. 
                       It comes with precomputed state-of-the-art audio-visual features from billions of frames 
                       and audio segments, designed to fit on a single hard disk.`,
        "topics":[],
        "problem_types":[c.COMPUTER_VISION, c.VIDEO, c.CLASSIFICATION, c.MULTILABEL],
        "website_url": "https://research.google.com/youtube8m/download.html",
        "paper_url": "https://research.google.com/youtube8m/workshop.html",
    },
    { 
        "title": "Scene Classification - AI Challegner",
        "type": c.DATASET,
        "description":"",
        "topics":[c.NATURAL_SCENES],
        "problem_types":[c.COMPUTER_VISION, c.CLASSIFICATION, c.MULTILABEL],
        "website_url": "https://challenger.ai/competition/scene/subject?lan=en",
        "paper_url": null,
    },
    { 
        "title": "Human Skeletal System Keypoints Detection - AI Challegner",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[c.COMPUTER_VISION, c.CLASSIFICATION, c.KEYPOINT],
        "website_url": "https://challenger.ai/competition/keypoint/subject?lan=en",
        "paper_url": null,
    },
    { 
        "title": "Image Captioning (Chinese) - AI Challenger",
        "type": c.DATASET,
        "description":"",
        "topics":[],
        "problem_types":[c.COMPUTER_VISION, c.CLASSIFICATION, c.CAPTIONING],
        "website_url": "https://challenger.ai/competition/caption/subject?lan=en",
        "paper_url": null,
    },
    { 
        "title": "StreetStyle-27K",
        "type": c.DATASET,
        "description":"",
        "topics":[c.FASHION],
        "problem_types":[c.COMPUTER_VISION, c.CLASSIFICATION],
        "website_url": "http://streetstyle.cs.cornell.edu/#dataset",
        "paper_url": "https://arxiv.org/abs/1706.01869",
    },
    { 
        "title": "Fashion-MNIST",
        "type": c.DATASET,
        "description":"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. We intend 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.",
        "topics":[c.FASHION],
        "problem_types":[c.COMPUTER_VISION, c.CLASSIFICATION],
        "website_url": "https://github.com/zalandoresearch/fashion-mnist",
        "paper_url": "http://arxiv.org/abs/1708.07747",
    },
]