SGG eval: R @ 20: 0.5099; R @ 50: 0.5933; R @ 100: 0.6170; for mode=predcls, type=Recall(Main).
SGG eval: ngR @ 20: 0.5942; ngR @ 50: 0.7655; ngR @ 100: 0.8537; for mode=predcls, type=No Graph Constraint Recall(Main).
SGG eval: zR @ 20: 0.0302; zR @ 50: 0.0616; zR @ 100: 0.0769; for mode=predcls, type=Zero Shot Recall.
SGG eval: mR @ 20: 0.1591; mR @ 50: 0.1939; mR @ 100: 0.2080; for mode=predcls, type=Mean Recall.
I just follow your scripts/rel_train_BGNN_vg_predcls.sh, and we train it 70000 iterations.
In your paper the mr is 30.4 / 32.9. but here, it just is 19.39, 20.80.
I hope you can just explain it, or you can push your checkpoint and log.
The accuracy reported in your paper is what we have to compare with you, but this reproduction result makes it impossible to carry out our comparison and we hope to get your help
SGG eval: R @ 20: 0.5099; R @ 50: 0.5933; R @ 100: 0.6170; for mode=predcls, type=Recall(Main).
SGG eval: ngR @ 20: 0.5942; ngR @ 50: 0.7655; ngR @ 100: 0.8537; for mode=predcls, type=No Graph Constraint Recall(Main).
SGG eval: zR @ 20: 0.0302; zR @ 50: 0.0616; zR @ 100: 0.0769; for mode=predcls, type=Zero Shot Recall.
SGG eval: mR @ 20: 0.1591; mR @ 50: 0.1939; mR @ 100: 0.2080; for mode=predcls, type=Mean Recall.
I just follow your scripts/rel_train_BGNN_vg_predcls.sh, and we train it 70000 iterations.
In your paper the mr is 30.4 / 32.9. but here, it just is 19.39, 20.80.
I hope you can just explain it, or you can push your checkpoint and log.
The accuracy reported in your paper is what we have to compare with you, but this reproduction result makes it impossible to carry out our comparison and we hope to get your help