21–30 of 53 posts
Its interesting to see "technical debt" become a more common term. Is there a rigid definition for it? From the article: "Leaf is lean and tries to introduce minimal technical debt to your stack." What exactly does that mean?
> super-human image recognition That's a bold claim. As far as I know there was one paper that reported a model beating human scores in a specific test (imagenet, I believe). Whether that translates to "superhuman" results in general is followed by a very big question mark. In general I really struggle to see how any algorithm that learns from examples, especially one that minimises a measure of error against further…
It is much faster than humans
> super-human image recognition That's a bold claim. As far as I know there was one paper that reported a model beating human scores in a specific test (imagenet, I believe). Whether that translates to "superhuman" results in general is followed by a very big question mark. In general I really struggle to see how any algorithm that learns from examples, especially one that minimises a measure of error against further…
Computers could be better at assigning probabilities to ambiguous examples. In particular, for an image that is very ambiguous for most humans, maybe a computer would assign 99% probability to it (hence it would be only a little bit ambiguous).
> super-human image recognition That's a bold claim. As far as I know there was one paper that reported a model beating human scores in a specific test (imagenet, I believe). Whether that translates to "superhuman" results in general is followed by a very big question mark. In general I really struggle to see how any algorithm that learns from examples, especially one that minimises a measure of error against further…
Second the examples were produced by scraping Flickr. Then mechanical turkers were asked to confirm if the object was in the image or not.
There are many images that are kind of ambigious, or contain multiple objects, so humans don't do perfectly. One researcher tried to estimate human performance, and got about 5%. Which has been beaten by computers now, by a lot.
Without this information it's hard to make a useful comparison at all.
I'm completely new to ML and what real world applications it's suitable for. Are we at the point yet where you can train a computer to look at arbitrary images and count the number of people in it? What if it was the largely on the same background and only the number of people were changing -- for example, a camera shooting a queue of people to determine queue depth at a bus station.
1. The "same background" doesn't really exist for most cameras in most settings. Changes in illumination alone will make segmenting the background tricky. Moving objects in the scene will also be hard - think fountains and trees in the wind. Google for "foreground-background segmentation" to see some papers on this.
2. I haven't seen anyone use recent ML algorithms with less than high quality images. That may not matter, but it could matter a lot.
3. Extending recent ML algorithms to work with video at a high enough frame rate to be useful (10Hz at a minimum) may or may not be easy.
I'm sure that what you're proposing could be done. But I think that the number of small annoyances you'd hit would probably discourage most people who aren't treating the problem as a research exercise in Computer Vision.