Introducing the Open Images Dataset by Ivan Krasin and Tom Duerig.
From the post:
In the last few years, advances in machine learning have enabled Computer Vision to progress rapidly, allowing for systems that can automatically caption images to apps that can create natural language replies in response to shared photos. Much of this progress can be attributed to publicly available image datasets, such as ImageNet and COCO for supervised learning, and YFCC100M for unsupervised learning.
Today, we introduce Open Images, a dataset consisting of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories. We tried to make the dataset as practical as possible: the labels cover more real-life entities than the 1000 ImageNet classes, there are enough images to train a deep neural network from scratch and the images are listed as having a Creative Commons Attribution license*.
The image-level annotations have been populated automatically with a vision model similar to Google Cloud Vision API. For the validation set, we had human raters verify these automated labels to find and remove false positives. On average, each image has about 8 labels assigned. Here are some examples:
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Impressive data set, if you want to recognize a muffin, gherkin, pebble, etc., see the full list at dict.csv.
Hopeful the techniques you develop with these images will lead to more focused image recognition. 😉
I lightly searched the list and no “non-safe” terms jumped out at me. Suitable for family image training.