If you wish to create an image classifier and want to use the data from Google Image Search results, and want to exclude some of the images, you can use this bookmarklet gi2ds (drag it to your bookmarks bar and click on it after your search). Then you can click on the images you want to exclude. A list is generated for you with all the relevant image-urls for you to process further.
gi2ds is intened to help you when creating an image dataset based on a google images query. It allows you to exclude images that are not relevant by toggling them on and off by clicking on them. Default is that all images are included. The urls are found in a popup down to the right. To get all available images you need to scroll all the way down for more images to load, also pressing the show more results button and continuing scrolling in order to get all the pictures available.
OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject. It consists of 5,957 multiple-choice elementary-level science questions (4,957 train, 500 dev, 500 test), which probe the understanding of a small “book” of 1,326 core science facts and the application of these facts to novel situations. For training, the dataset includes a mapping from each question to the core science fact it was designed to probe. Answering OpenBookQA questions requires additional broad common knowledge, not contained in the book. The questions, by design, are answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. Strong neural baselines achieve around 50% on OpenBookQA, leaving a large gap to the 92% accuracy of crowd-workers.
The data seems to come from a lot of research projects where they have used different machine learning techniques to analyse the data.
Now that we have a lot better means of using machine learning and we have easy access to a lot of related data and our compute power has increased dramatically it might be that we will see quite a few improvements to older research results. I welcome this initiative and believe that the world will become a better place due to us collectively solving the worlds many problems using AI.
Github user andri27-ts has put together materail for learning Deep Reinforcement Learning in 60 days. If you find DeepMinds breakthroughs with thyr AlphaGo Zero and OpenAI’s Dota 2 facinating and want to learn how they work, the repository offers resources and project suggestions.
It contains over 100,000 HD video sequences, that make up over a thousand hours of footage. The data contains over 100 000 annotated images for object detection for bus, traffic light, traffic sign, person, bike, truck, motor, car, train, and rider. Alos segmentation, drivable area, lane markings etc.
I love how data is released to the public for the greater good.