3 ms·
> I chose MobileNetV2 to make iteration faster. When I tried ResNet50 or other larger models the gap between the M1 and Nvidia grew wider. (and that's on CIFAR
by lopuhin 6y ago
> I chose MobileNetV2 to make iteration faster. When I tried ResNet50 or other larger models the gap between the M1 and Nvidia grew wider.
(and that's on CIFAR-10). But why not report these results and also test on a more realistic datasets? The internet is full of M1 TF brenchmarks on CIFAR or MNIST, has anyone seen something different?
- sillysaurusx 6y agoHehe. That criticism could be applied to ML itself. :) I wish ML used more than CIFNISTNet, but unfortunately there's not a lot of standard datasets yet. (Even Imagenet is an absolute pain to set up.)
- sdenton4 6y agoTensorflow Datasets includes a lot of the 'standard' datasets in a way that's dead simple to call up and use (including ~10 variants of imagenet): https://www.tensorflow.org/datasets/catalog/overview https://www.tensorflow.org/datasets/catalog/overview