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26 Open 12 Closed
26 Open 12 Closed
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Issues list

predict_on_images.py error
#53 opened Nov 29, 2020 by brightbsit
eval_sentence_predictions.py: error: too few arguments
#52 opened Jun 3, 2018 by NEUdeep
3
multi-bleu.perl
#49 opened Mar 6, 2017 by ilkerkesen
image captioning
#48 opened Jun 10, 2016 by shivadehghani
vgg_feats.mat file is being created but its having very small size of 194bytes. Why so?
#47 opened May 18, 2016 by amangupta199334
Can we use levelDB features extracted using C++ utility at the place of vgg_feats.mat?
#46 opened Apr 21, 2016 by amangupta199334
Why optimizing the Ws matrix directly?
#45 opened Apr 14, 2016 by meanmee
Incorrect prediction while testing.
#44 opened Mar 27, 2016 by amangupta199334
when i train a batch(batchsize 100) of flickr8k, it takes around 0.8 seconds,
#43 opened Jan 14, 2016 by evercherish
4
How can i use this code to train regions & snippets RNN model?
#42 opened Dec 8, 2015 by lebronze
3
Maybe a mistake in lstm_generator.py
#41 opened Nov 19, 2015 by liu09114
2
Confusion about an equation in the paper
#38 opened Nov 13, 2015 by sunshineatnoon
Use for sentence input to sentence output
#36 opened Nov 10, 2015 by mikeconnors909
Please someone explain the magic behind this
#35 opened Nov 2, 2015 by inancgumus
predict_on_images.py: error: too few arguments
#33 opened Oct 28, 2015 by RanAR90
1
change in extract_features needed due to caffe update
#31 opened Sep 8, 2015 by samim23
1
training over new dataset
#25 opened Jul 12, 2015 by agoel-cs
4
py_caffe_feat_extract
#24 opened Jul 1, 2015 by lichengunc
multiple hosts
#23 opened Jun 24, 2015 by lichengunc
Size of Descriptive Sentences
#20 opened Jun 2, 2015 by cicero19
1
Problems in gradient check
#19 opened May 30, 2015 by ShijianTang
Best hyperparameters for RNN model
#18 opened May 30, 2015 by ShijianTang
Transfer Learning with word2vec?
#10 opened Mar 15, 2015 by cemoody
1
R Kelly
#6 opened Feb 5, 2015 by JesseScott
Aborting, cost seems to be exploding.
#4 opened Jan 8, 2015 by pannous
4
ProTip! Adding no:label will show everything without a label.