A company is developing a new image classification model that categorizes photos into three classes: 'Animals', 'Nature', and 'Urban'. They noticed that their model struggles to correctly classify images of animals in urban settings. Which multi-class loss function would best help them optimize their model's performance in this scenario?

The best choice is the softmax cross‑entropy loss, possibly with class‑weighting or a focal‑loss variant, because it measures the probability gap between the true class and all other classes and gives stronger penalties when the model is confident but wrong. By adding a weight or focal factor, the loss can be tuned to focus more on hard examples like animals in urban scenes, which are currently misclassified. This encourages the network to learn finer distinctions between the “Animals” class and the “Urban” class when they overlap. For instance, if an image of a dog in a city park is wrongly predicted as “Urban,” the loss will be large and push the model to correct that mistake.