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Following the author's instruction, if anyone use ./experiments/caption_lstm/train.sh to train the network without enough RAM, it won't result in meaningful error message -- just produce some empty caffemodel.h5 files and says it cannot load the weights as something got wrong. If you take a look of RAM usage using software such as htop you will find the memory is exploding.
To solve it, you need to refer to the second points of this segment: replace module: "rcnn_layers" with module: "efficient_rcnn_layers".
I think it's because the Process managed the memory usage and caught the errors so that it did not produce any useful error message.
The text was updated successfully, but these errors were encountered:
Following the author's instruction, if anyone use
./experiments/caption_lstm/train.sh
to train the network without enough RAM, it won't result in meaningful error message -- just produce some emptycaffemodel.h5
files and says it cannot load the weights as something got wrong. If you take a look of RAM usage using software such ashtop
you will find the memory is exploding.To solve it, you need to refer to the second points of this segment: replace
module: "rcnn_layers"
withmodule: "efficient_rcnn_layers"
.I think it's because the
Process
managed the memory usage and caught the errors so that it did not produce any useful error message.The text was updated successfully, but these errors were encountered: