3 ms·

Performance is in ms, see http://caffe.berkeleyvision.org/performance_hardware.html http://caffe.berkeleyvision.org/performance_hardware.html Lookup the testing

by pilooch 11y ago

Performance is in ms, see http://caffe.berkeleyvision.org/performance_hardware.html http://caffe.berkeleyvision.org/performance_hardware.html Lookup the testing numbers, 500 images / sec. Character-based models are way simpler, you can use test very easily using the original link to the model. They are not easily gamed either: they learn features as convolutional filters over the characters. These filters are more powerful than ngrams, as complex as the filters you may have seen for images. Now, about gaming the nets, there's an issue, which is the same as for image-based CNNs: some combinations of letters (or pixel) do exist that do not make a difference to the eye, but push the classifier into the wrong class. However, in order to find these combinations, having a first-hand on the underlying neural net and its weights is mandatory.

colejohnson66 11y ago
Would crowd sourcing help? Like, what if when you block an element on a page, it asks if what you're blocking is an ad (or something else "no one" wants to see). If they click yes, it sends that URL or whatever to the neural net servers. If enough people block that element, it'll get blocked.
- pilooch 11y ago
  Possibly. Having millions of URLs to populate each class is a good thing to start with. Gathered through other means, our current dataset has around 10M URLs in the 'ads' category. The model we made available to the public was built from 2M of these URLs. EDIT: of possible interest is that these models output a probability and possibly a confidence of having a URL blocked. Base on these, an blocker could ask for confirmation.
argonaut 11y ago
The performance numbers are not representative of your proposed workload. They are 1) images either from RAM or sequentially on disk that are 2) batched and tested together with vector operations all the way (memory bandwidth also dominates the latency for this reason), and 3) the images are only 256 x 256 pixels (much smaller than a website). Also, the neural net occupies something like 1.2 gigs of RAM, and something like 3 gigs of GPU memory.
- pilooch 11y ago
  > much smaller than a website The character-based net reads the URL, not the website. The common alphabet used in the model made public is of size 69, URL length can be for example 256. Not only this is below the image size you are talking about, but after the first convolution the alphabet (in one-hot vector encoding) is collapsed, which leaves convolutions across length 256 in 1D. Not that terrible. In proxy-like territory, passing batches of URLs to the GPUs could pave the way to new ad-blockers at large. To be tested :)
  - argonaut 11y ago
    I was referring to the hypothetical image classifier, not the URL text classifier (the URL text classifier, as I mentioned, would perform very poorly).

by pilooch 11y ago

colejohnson66 11y ago

Would crowd sourcing help? Like, what if when you block an element on a page, it asks if what you're blocking is an ad (or something else "no one" wants to see). If they click yes, it sends that URL or whatever to the neural net servers. If enough people block that element, it'll get blocked.

pilooch 11y ago
Possibly. Having millions of URLs to populate each class is a good thing to start with. Gathered through other means, our current dataset has around 10M URLs in the 'ads' category. The model we made available to the public was built from 2M of these URLs. EDIT: of possible interest is that these models output a probability and possibly a confidence of having a URL blocked. Base on these, an blocker could ask for confirmation.

argonaut 11y ago

The performance numbers are not representative of your proposed workload. They are 1) images either from RAM or sequentially on disk that are 2) batched and tested together with vector operations all the way (memory bandwidth also dominates the latency for this reason), and 3) the images are only 256 x 256 pixels (much smaller than a website). Also, the neural net occupies something like 1.2 gigs of RAM, and something like 3 gigs of GPU memory.

pilooch 11y ago
> much smaller than a website The character-based net reads the URL, not the website. The common alphabet used in the model made public is of size 69, URL length can be for example 256. Not only this is below the image size you are talking about, but after the first convolution the alphabet (in one-hot vector encoding) is collapsed, which leaves convolutions across length 256 in 1D. Not that terrible. In proxy-like territory, passing batches of URLs to the GPUs could pave the way to new ad-blockers at large. To be tested :)
- argonaut 11y ago
  I was referring to the hypothetical image classifier, not the URL text classifier (the URL text classifier, as I mentioned, would perform very poorly).