How hard are computer vision datasets? Calibrating dataset difficulty to viewing time

David Mayo, Jesse Cummings, Xinyu Lin, Dan Gutfreund, Boris Katz, Andrei Barbu

Keywords: deep learning, computer vision, cognitive science, datasets

TL;DR: We develop a new dataset difficulty metric based on how long humans must view an image in order to classify a target object, finding that the distribution of current datasets is skewed towards easy images.

Abstract: Humans outperform object recognizers despite the fact that models perform well on current datasets. Numerous attempts have been made to create more challenging datasets by scaling them up from the web, exploring distribution shift, or adding controls for biases. The difficulty of each image in each dataset is not independently evaluated, nor is the concept of dataset difficulty as a whole well-posed. We develop a new dataset difficulty metric based on how long humans must view an image in order to classify a target object. Images whose objects can be recognized in 17ms are considered to be easier than those which require seconds of viewing time. Using 133,588 judgments on two major datasets, ImageNet and ObjectNet, we determine the distribution of image difficulties in those datasets, which we find varies wildly, but significantly undersamples hard images. Rather than hoping that distribution shift or other approaches will lead to hard datasets, we should measure the difficulty of datasets and seek to explicitly fill out the class of difficult examples. Analyzing model performance guided by image difficulty reveals that models tend to have lower performance and a larger generalization gap on harder images. Encouragingly for the biological validity of current architectures, much of the variance in human difficulty can be accounted for given an object recognizer by computing a combination of prediction depth, c-score, and adversarial robustness. We release a dataset of such judgments as a complementary metric to raw performance and a network’s ability to explain neural recordings. Such experiments with humans allow us to create a metric for progress in object recognition datasets, which we find are skewed toward easy examples, to test the biological validity of models in a novel way, and to develop tools for shaping datasets as they are being gathered to focus them on filling out the missing class of hard examples from today’s datasets. Dataset and analysis code can be found at https://github.com/image-flash/image-flash-2022.

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