cdancette / rubi.bootstrap.pytorch

NeurIPS 2019 Paper: RUBi : Reducing Unimodal Biases for Visual Question Answering

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Could you provide pre-trained weights for baseline architecture?

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Would it be possible to provide the checkpoint for baseline architecture as well?

I got very low accuracy (only 21.74) with baseline.yaml:

[S 2019-08-22 13:44:22] ...trap/engines/engine.py.120: eval_epoch.epoch: 11
[S 2019-08-22 13:44:22] ...trap/engines/engine.py.120: eval_epoch.loss: 4.28
[S 2019-08-22 13:44:22] ...trap/engines/engine.py.120: eval_epoch.accuracy_top1: 21.74
[S 2019-08-22 13:44:22] ...trap/engines/engine.py.120: eval_epoch.accuracy_top5: 70.43

which is very puzzling.

Instead of looking at the accuracies printed, I just had to use a proper evaluation script: #4