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Extracting Detectron Features #1
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Thank you for your interest! I just added the code I used for extracting features for NLVR2. Hope you could find that useful! |
Hi, can you provide the code about how to extract the features in text and pictures, I will very appreciated for the code , thanks ! |
Hi, thank you for your interest in our code! Just to be sure, do you mean extracting fixed hidden features from the transformer given an image and text? If so, sorry that I did not implement that particular function. I would suggest taking a look at pytorch_pretrained_bert/modeling.py at line 1326. I think "encoded_layers" should be the hidden features of the transformer. Please feel free to comment should you have any more questions! |
VQA also use the detectron fitures I found in your Readme.md. It is about 160g by using wget |
Hi @alice-cool, @MinghuiAn, @sanjayss34 , how is the speed of extracting image features? For example, by comparison with one GPU (cpu-only is presumably not tolerable), for NLVR2 107,292 images, lxmert takes 5-6 hours to extract faster-rcnn features by this caffe. I also follow #10. |
@sanjayss34 @liunian-harold-li @yezhengli-Mr9 |
Hello, could you tell me how to get image features for my pictures? I don't know how to make it. |
Is there a file "/visualbert/detectron/core/test.py"? I can't find it in the project |
Hi, thanks for releasing your code soon after your paper and for making your evaluations easy to reproduce! Could you please provide more detail on how you extracted the Detectron features? I don't see a straightforward way to extract the features with the existing code in the Detectron repository. Thanks!
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