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Does progress on ImageNet transfer to real-world datasets? We investigatethis question by evaluating ImageNet pre-trained models with varying accuracy(57% - 83%) on six practical image classification datasets. In particular, westudy datasets collected with the goal of solving real-world tasks (e.g.,classifying images from camera traps or satellites), as opposed to web-scrapedbenchmarks collected for comparing models. On multiple datasets, models withhigher ImageNet accuracy do not consistently yield performance improvements.For certain tasks, interventions such as data augmentation improve performanceeven when architectures do not. We hope that future benchmarks will includemore diverse datasets to encourage a more comprehensive approach to improvinglearning algorithms.