English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Camera traps are crucial in biodiversity motivated studies, however dealingwith large number of images while annotating these data sets is a tedious andtime consuming task. To speed up this process, Machine Learning approaches area reasonable asset. In this article we are proposing an easy, accessible,light-weight, fast and efficient approach based on our winning submission tothe "Hakuna Ma-data - Serengeti Wildlife Identification challenge". Our systemachieved an Accuracy of 97% and outperformed the human level performance. Weshow that, given relatively large data sets, it is effective to look at eachimage only once with little or no augmentation. By utilizing such a simple, yeteffective baseline we were able to avoid over-fitting without extensiveregularization techniques and to train a top scoring system on a very limitedhardware featuring single GPU (1080Ti) despite the large training set (6.7Mimages and 6TB).

Mastering Large Scale Multi-label Image Recognition with high efficiency overCamera trap images