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The mAP value generated by the compute_framelevel_mAP.m is 0.1409, lower than the value 44.4 reported on you paper. If I replace the model in the xfeat.sh from thumos_CDC/convdeconv-TH14_iter_24390 to sports1m_C3D/conv3d_deepnetA_sport1m_iter_1900000, the generated mAP becomes 0.0171. I am confused why the reproduced mAP value significantly lower than the reported value. May you suggest any idea?
The text was updated successfully, but these errors were encountered:
Some people have already reproduced the results on both per-frame labeling an temporal localization tasks. You may want to check step by step in detail. BTW, it doesn't make sense to me why replace the trained model with the pre-trained sports1m model...
The mAP value generated by the compute_framelevel_mAP.m is 0.1409, lower than the value 44.4 reported on you paper. If I replace the model in the xfeat.sh from thumos_CDC/convdeconv-TH14_iter_24390 to sports1m_C3D/conv3d_deepnetA_sport1m_iter_1900000, the generated mAP becomes 0.0171. I am confused why the reproduced mAP value significantly lower than the reported value. May you suggest any idea?
The text was updated successfully, but these errors were encountered: