Hi,
Dowloaded your features, trained and evaluated you model.
I get the following results
train subset video numbers: 9649
validation subset video numbers: 4728
BMN training loss(epoch 0): tem_loss: 1.152, pem_loss: 0.267, total_loss: 1.419
BMN testing loss(epoch 0): tem_loss: 1.184, pem_loss: 0.261, total_loss: 1.445
BMN training loss(epoch 1): tem_loss: 1.095, pem_loss: 0.235, total_loss: 1.329
BMN testing loss(epoch 1): tem_loss: 1.115, pem_loss: 0.231, total_loss: 1.346
BMN training loss(epoch 2): tem_loss: 1.070, pem_loss: 0.223, total_loss: 1.293
BMN testing loss(epoch 2): tem_loss: 1.098, pem_loss: 0.249, total_loss: 1.347
BMN training loss(epoch 3): tem_loss: 1.056, pem_loss: 0.218, total_loss: 1.274
BMN testing loss(epoch 3): tem_loss: 1.103, pem_loss: 0.222, total_loss: 1.325
BMN training loss(epoch 4): tem_loss: 1.042, pem_loss: 0.213, total_loss: 1.255
BMN testing loss(epoch 4): tem_loss: 1.098, pem_loss: 0.225, total_loss: 1.323
BMN training loss(epoch 5): tem_loss: 1.028, pem_loss: 0.210, total_loss: 1.238
BMN testing loss(epoch 5): tem_loss: 1.116, pem_loss: 0.260, total_loss: 1.377
BMN training loss(epoch 6): tem_loss: 1.016, pem_loss: 0.212, total_loss: 1.229
BMN testing loss(epoch 6): tem_loss: 1.101, pem_loss: 0.223, total_loss: 1.324
BMN training loss(epoch 7): tem_loss: 0.999, pem_loss: 0.205, total_loss: 1.204
BMN testing loss(epoch 7): tem_loss: 1.150, pem_loss: 0.222, total_loss: 1.372
BMN training loss(epoch 8): tem_loss: 0.991, pem_loss: 0.204, total_loss: 1.195
BMN testing loss(epoch 8): tem_loss: 1.138, pem_loss: 0.221, total_loss: 1.359
BMN training loss(epoch 9): tem_loss: 0.926, pem_loss: 0.191, total_loss: 1.117
BMN testing loss(epoch 9): tem_loss: 1.163, pem_loss: 0.216, total_loss: 1.379
BMN training loss(epoch 10): tem_loss: 0.901, pem_loss: 0.188, total_loss: 1.089
BMN testing loss(epoch 10): tem_loss: 1.176, pem_loss: 0.215, total_loss: 1.391
BMN training loss(epoch 11): tem_loss: 0.886, pem_loss: 0.187, total_loss: 1.073
BMN testing loss(epoch 11): tem_loss: 1.218, pem_loss: 0.216, total_loss: 1.434
BMN training loss(epoch 12): tem_loss: 0.873, pem_loss: 0.185, total_loss: 1.059
BMN testing loss(epoch 12): tem_loss: 1.237, pem_loss: 0.216, total_loss: 1.453
BMN training loss(epoch 13): tem_loss: 0.863, pem_loss: 0.185, total_loss: 1.048
BMN testing loss(epoch 13): tem_loss: 1.265, pem_loss: 0.217, total_loss: 1.482
BMN training loss(epoch 14): tem_loss: 0.853, pem_loss: 0.183, total_loss: 1.037
BMN testing loss(epoch 14): tem_loss: 1.257, pem_loss: 0.217, total_loss: 1.474
[INIT] Loaded annotations from validation subset.
Number of ground truth instances: 7293
Number of proposals: 472632
Fixed threshold for tiou score: [0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]
[RESULTS] Performance on ActivityNet proposal task.
Area Under the AR vs AN curve: 63.50907719731249%
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will
always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
"Adding an axes using the same arguments as a previous axes "
AR@1 is 0.31546688605512135
AR@5 is 0.43129027834910183
AR@10 is 0.5023584258878377
AR@100 is 0.7298642533936651
In your paper the Area Under the AR vs AN curve is 67.10 and I'm getting 63.5
and your AR@100 is 75.01 and I'm getting 72.9.
I didn't change your configuration.
Any idea why I'm getting lower results.
Thanks
Ophir