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Re: fmow_baseline (response to post by fMoW_baseline) | Reply
Hey!

It turns out it was all confusion.

There was some confusion between the comments in the code and the actual intent behind the code extraction. data_ml_functions/mlFunctions.py#L175 says "Custom generator that yields a vector containign the 4096-d CNN codes output by ResNet50 and metadata features...", which led to me generating codes that were supposed to be 4096-d, hence I edited the method to reflect that.

I think the comment is a vestige from the old baseline, and the intent was to create 2208-d codes + metadata. This was what was causing the error, and once I fixed that, it all worked.

Would you be able to push a commit that updates that comment?
Thanks! And sorry again for the confusion. Since I'm using make_parallel, I'm having to edit a lot of the code and got caught up in the comments instead of the single GPU model summery.
Re: fmow_baseline (response to post by Ritwik_G) | Reply
Ah, great catch! We didn't update the comments. Apologies for the confusion. We'll update those soon. Thanks!

As for using make_parallel, there shouldn't be many changes required. We changed very few lines to remove that.
Re: fmow_baseline (response to post by fMoW_baseline) | Reply
Hi guys,

could you please specify where the improved performance (as compared to the first baseline) comes from ?
Re: fmow_baseline (response to post by Mloody2000) | Reply
The following diff shows most of the important changes: https://github.com/fMoW/baseline/commit/17d86046be202f46f6e6604f0ca47652770315ab.

We believe better handling of spatial context, metadata features, and using DenseNet as our feature extractor were the primary factors that improved performance.
Re: fmow_baseline (response to post by fMoW_baseline) | Reply
Would it be possible to publish the 3 files

data/working/dataset_stats.json
data/working/cnn_codes_stats_no_metadata.json
data/working/cnn_codes_stats_with_metadata.json

used by the pre-trained models?
I think they're necessary for reproducing the results exactly.
Re: fmow_baseline (response to post by pfr) | Reply
The following command, using the baseline code and the fMoW-rgb dataset with val sample false_detection boxes, will generate these files for you:
python runBaseline.py -prepare


If you would like, we can release these files after the challenge ends. We don't want to release these files now as we are very close to the end of the challenge and they may provide an unfair advantage.
Re: fmow_baseline (response to post by fMoW_baseline) | Reply
Can you release them now? I would like to understand why I wasn't able to reproduce the exact baseline score.
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