AI-enabled products that can record and monitor African wildlife come with their share of challenges. In addition to requiring massive amounts of training data, the diversity of the data must account for species, landscape, cultural relevance and human influence. “We ran into a problem when we were trying to detect cows in the imagery. We had a ton of pictures of cows from Washington, where we are, but cows look different in Africa,” Gracie Ermi, Research Software Engineer at Vulcan, pointed out. “Diversity in the dataset has been super challenging.”
How Vulcan Improved Turnaround Times to Process Training Datasets
