Get it for free! The first open source precious data set, which takes a lot of time and manual cleaning and annotation, machine vision enthusiasts come and get it

Generally speaking, the more accurately and quantitatively the data is labeled, the better the model will perform. Naturally, the effect of the product will be better. The first annual professional AI competition - 2016 Shanghai BOT Big Data Application Competition (www.datadreams.org), in order to provide participants with better competition data, a data collection and labeling group was specially established, which took a lot of time and manpower to carry out the competition. Clean, categorize, label, and get the dataset that everyone sees. High-quality annotated data provides contestants with better choices and experiences. In order to promote the innovation of algorithms and applications and provide high-quality data resources for big data artificial intelligence enthusiasts, the competition will open source precious data sets for the first time. (Please click http://www.datadreams.org/review.html for download)
The image set is a data set used for the Hackathon Machine Vision Question Answering (VQA) competition, including 12 animals and 5 objects, including test The set gives image data and Json files, including 997 image data; the training set contains 7066 images, of which toy.zip is the demonstration image of the puppet (502), and imagenet.zip is the reference object photo in imagenet (6485) ,sencesexample.zip is a scene example map (79).
In addition, the 2nd annual professional AI competition - 2017 China Big Data Artificial Intelligence Innovation and Entrepreneurship Competition (www.datadreams.org) launched the Pathological Slice Recognition AI Challenge of the BOT Competition Series and the Robo Advisory Technology Challenge of the BOT Competition Series Competition, 200,000 cash prizes, tens of millions of venture capital prize pools, massive precious data sets, recruit AI heroes from all over the world, and challenge pathological diagnosis and robo-advisors! The pathological slice recognition AI challenge gastric cancer pathological digital sample annotation data set has been opened, come and watch (http://www.datadreams.org/race-data-3.html)!