mat_kelcey

4 ms·

1.
by mat_kelcey 8y ago
"The system looks at sets of pictures of the hive door taken every 10 seconds. It then extrapolates out the background, assesses the objects that have moved in the frame, and then counts the things that are likely to be bees." &qu
2.
by mat_kelcey 8y ago
Just a randomly sampled 32x32 patch, intution being most bees in image aren't bigger
3.
by mat_kelcey 8y ago
That's just a sanity check; the end goal is some accelerated hardware ( neural ompute stick / jevois / etc)
4.
by mat_kelcey 8y ago
no, it won't directly. conv nets handle translation invariance but not scale invariance. having said that there's no reason you can't use aggressive data augmentation for this (resizing before patch sampling). i wonder how mu
5.
by mat_kelcey 8y ago
yeah, that's true it's only counting bees in one image, which i thought was part 1 of any of these other things. can you post your approach / code to doing any of these things you mention? i'd love to see what you'v
6.
Learning to do laps with reinforcement learning and neural nets(matpalm.com)
2 points by mat_kelcey 11y ago|0 comments
7.
by mat_kelcey 15y ago
How is the $150 broken down?
8.
by mat_kelcey 15y ago
+1
9.
by mat_kelcey 15y ago
I think the Lithuania bit is a false alarm. The pipeline needs a lot more work and investigation of results. The main thing I was focusing on first was scaling it out.
10.
by mat_kelcey 15y ago
oh your irony...
11.
by mat_kelcey 15y ago
I was using the 2011-07-22 set so the data's already a bit stale...