An extended version of the previous ICCV2015 challenge dataset. It contains 8,000 images each displaying a single individual, labeled with the apparent age. Each image has been labeled by multiple individuals, using a collaborative Facebook implementation and Amazon Mechanical Turk. The votes variance is used as a measure of the error for the predictions. This is the first state of the art database for Apparent Age Recognition rather than Real Age recognition. In this first stage, we provide two ZIP files containing the images for the training and validation sets. The participants will evaluate their methods in the validation set. Then, in the second stage we will provide the test set to measure their final results.
A more detailed information is provided on the data section of the competition.
- Scripts: With the data, the organizers provides a set of scripts to facilitate the access to the data and use the evaluation metrics. More information is provided on the data page.
- Contact: In order to clarify any doubt or to ask general assistance, you can use the forum in the Codalab competition. You can contact the organizers email@example.com.