Could anyone be kindly to help me on this issue? thanks:
We have lots of photos/images, say 10 million or more, they are original photos/images from our customers which need to be protected(To prevent plagiarism), here we call it as dataset A.
We also got lots of images by way of web crawler, from bloggers, websites, forum, etc. some of these images are simply copied from dataset A, some added with additional watermark, we call it as dataset B. it currently contains about 300000 images, but will grow day by day.
We will use 1 image or several images from dataset A, we call it as dataset C, we want to search images in B which is similar with C, and list all similar images.
We want to use deep learning for similarity search, but most of the images in dataset A has no tag, could we train these images into a specific model, then we could get more accurate result while searching similar images?
Thanks a lot for your patience to read this long requirement, and have a nice day!
dp, when you create your
dp.Optimizerobjection, you define a function called
callbackfunction is executed after every batch on the training set, and it performs the actual parameter updates. Your script uses the simple callback from one fo the
dpexamples. If you want a learning pattern other than simple SGD - like adding momentum, norms, cutoffs, etc. - you do it in
dpby modifying the
I would recommend taking a look at torchnet if you like dp's style. For myself, I now prefer to do without either, and just write my own training scripts (more flexible in the end).
Could you please be more explicit and include examples to illustrate you statement ?
It must be easier to train non standard models such as adversarial networks on a dedicated architecture, but it takes a lot of time to code/test.
Would it possible to add a plugin to dp / torchnet for doing that ?