On Wed, Jun 16, 2021 at 2:44 PM Chris Angelico <ros...@gmail.com> wrote:
> On Thu, Jun 17, 2021 at 7:35 AM Dan Stromberg <drsali...@gmail.com> wrote: > > > >> How well can you define the things you're looking for? > > > >> > > > >> https://xkcd.com/1425/ > > > >> > > > > He means that image processing is a hard problem that requires > expertise > > > to solve. > > > > > > > > > > > Here is the image, I need to separate the road and markings from the > > > rest and divide the image into squares of 100x100 pixels, for each > square I > > > need to check if it contains a road and markings: > > > > > > Can you define road in terms of an algorithm that looks at the pixels? > > > > > > > I think that XKCD may be a little out of date. > > It's not out of date. The task still requires a lot of effort - it's > just that the effort is now "preparing a suitable corpus" rather than > "figuring out how on earth to do this". Even with all the tools at our > disposal, there's still a stark (and often surprising) distinction > between the easy and the hard. > Well... Are you sure? It's no longer a problem that requires 5 years and a research team. It's now a problem that requires hunting for relevant labeled data, and failing that, paying a small team of unskilled laborers minimum wage to classify images from google images or similar. Plus some programming to create the model and to use it in production. Deep Learning is catching on, in significant part, because lots of useful data is becoming available. Also because hardware is getting faster and the algorithms have improved. Clearly some things are harder than others, but the "hard" example given in the XKCD is no longer really a good example. -- https://mail.python.org/mailman/listinfo/python-list
