Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Commented section in feature_extract.py #13

Open
goncalomcorreia opened this issue May 22, 2017 · 2 comments
Open

Commented section in feature_extract.py #13

goncalomcorreia opened this issue May 22, 2017 · 2 comments

Comments

@goncalomcorreia
Copy link

Could you update the project with the code on how to get the features from the Caffe VGGNet-19 model? In preprocess_data.py, it reads:

train_feat_file = 'train2014_fixed_fc7_feat.pkl'
val_feat_file = 'val2014_fixed_fc7_feat.pkl'

but there's no explanation on how to get these features. Could you elaborate? I guess they were obtained by the Caffe model referenced in feature_extract.py, but the commented section is incomplete.

@xiaomengyc
Copy link

I found this problem too. The output of VGG features are very sparse, but the author published data here is apparently different from that. I have been in this problem for days, hope the author can publish these codes or give some tips!

@zcyang
Copy link
Owner

zcyang commented Jun 13, 2017

The features are extracted with caffe, with the script:
https://github.com/zcyang/imageqa-san/blob/master/data_vqa/feature_extract.py

Since the features are sparse, they are stored in sparse matrix format:
https://github.com/zcyang/imageqa-san/blob/master/src/data_provision_att_vqa.py#L74

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants