Fig. 7.

(A–I) Shown are nine images the ResNet-152 model labeled incorrectly. Above each image are a combination of expert-provided labels (for the species type and counts) and volunteer-provided labels (for additional attributes), as well as the model’s prediction for that image. Below each image are the top guesses of the model for different tasks, with the width of the color bars indicating the model’s output for each of the guesses, which can be interpreted as its confidence in that guess. One can see why the images are difficult to get right. G and I contain examples of the noise caused by assigning the label for the capture event to all images in the event. A, B, D, and H show how animals being too far from the camera makes classification difficult.