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Let's remember though that Imagenet is not a good representation of reality. See e.g. performance on OjectNet, https://objectnet.dev/ https://objectnet.dev/, w
by notemaker 6y ago
Let's remember though that Imagenet is not a good representation of reality.
See e.g. performance on OjectNet, https://objectnet.dev/ https://objectnet.dev/, when trained on Imagenet. For the same classes, we see _dramatic_ drops in accuracy.
- t_serpico 6y agonice. glad to see this exists.
- YeGoblynQueenne 6y agoAlso see: Do ImageNet Classifiers Generalize to ImageNet? We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly "harder" images than those found in the original test sets. https://arxiv.org/abs/1902.10811 https://arxiv.org/abs/1902.10811