Nhabls
Nhabls t1_j5m4lxa wrote
Obviously haven't had the time to read through it, and this is a clear nitpick but i really don't like when sites like this force you to download the files rather than display it in the browser by default
Nhabls t1_j32qdjm wrote
Reply to comment by AlmightySnoo in [News] AMD Instinct MI300 APU for AI and HPC announced by samobon
AMD solutions have been in "development" for as long as i've been in contact with the space. The approaches rise and fall but never deliver fully. Maybe it'll be different in the future, who knows
Nhabls t1_j32q6zp wrote
The "monopoly" is from the ecosystem mostly, not the hardware itself. Practicioners and researchers have a much better time using consumer/entry level professional nvidia hardware. So they use nvidia.
Mind you that in the supercomputer level there is no real "monopoly" as those people just develop their solutions from the ground up.
Nhabls t1_iwcdek4 wrote
Reply to comment by Tiny-Mud6713 in [P] Need help with this CNN transfer learning problem by Tiny-Mud6713
The data doesn't seem that imbalanced, not to cause the issues you're having. And idk what you are using for augmentation but you can def augment classes to specifically solve imbalance ( i don't like doing that personally). My next guess would be looking at how you're splitting the data for train/val and/or freezing the vast majority of the pretrained model and maybe even just training on the last layer or 2 that you add on top.
Regardless, it's something that's useful to know (very frequent in real world datasets) here's a link that goes over how to weigh classes for such cases it's with tensorflow in mind but it's the same concept regardless
Nhabls t1_iwc3rap wrote
Reply to comment by Tiny-Mud6713 in [P] Need help with this CNN transfer learning problem by Tiny-Mud6713
You don't augment validation data, you'd be corrupting your validation scores, you'd only augment it at the end when/if you're training with all the data
Speaking of, look at your class representation %s, accuracy might be completely misleading if you have 1 or 2 overwhelmingly represented classes
Nhabls t1_iwc3gcr wrote
What is the representation of each class? A class imbalance could create this exact behavior. You dont even need to use a data augmentation technique ( i don't have a particularly great opinion of them, personally) and just scale the weights appropriately instead.
Also what does "Standard" mean here?
Nhabls t1_j6sbq3i wrote
Reply to comment by RandomCandor in [R] Faithful Chain-of-Thought Reasoning by starstruckmon
The arms race has been going for over a decade now...