10 ms·
ResNet-50 achieving a 60% accuracy on CIFAR-10. As far as I can tell, SOTA ("state of the art") is 99% on CIFAR-10 [1]. The first entry in wikipedia, for 2010,
by joe_the_user 5y ago
ResNet-50 achieving a 60% accuracy on CIFAR-10.
As far as I can tell, SOTA ("state of the art") is 99% on CIFAR-10 [1]. The first entry in wikipedia, for 2010, shows 21.1% error rate, so 78.9% accuracy. Which is to say a 60% accuracy rate is far from useful to put it mildly.
Essentially, with that low an accuracy rate, I don't think there's reason to think the predictions are meaningful in a practical sense. But maybe there's something I'm missing in accuracy description.
[1] https://en.wikipedia.org/wiki/CIFAR-10 https://en.wikipedia.org/wiki/CIFAR-10
- cs702 5y agoSame goes for ImageNet: These GNNs are getting only 50% top-5 accuracy, well below SOTA. But I find that figure impressive: It implies that I can concoct a newfangled architecture for visual recognition, put it through one of these GNNs, and begin training at ~50% top-5 accuracy. Also, AFAIK this is the first effort of its kind; I would expect the figures to improve over time, as usual.
- deleted 5y ago[deleted]
- joshuamorton 5y agoConsider that a random baseline is 10% accuracy, so 60% is pretty good, especially if the training time is functionally free to get to that accuracy. Granted the comparison is 2500 iterations of sgd which isn't a lot. I haven't played with cifar in a while, but that's probably not a ton of savings.