yanxp / asm-pytorch Goto Github PK

First, thank you for the awesome package!I encountered some problems in the experiment.

1. my experimental data is voc2007+voc2012. I trained 60,000 times with VOC 2007 initially and got a mAP of 0.5476, which was higher than that of the paper with 10% labeled samples. Could you tell me how big the mAP is when you do not label the samples?
2. The results obtained by adding random samples are similar to those obtained by your paper method. The test results of the model obtained by your method after 60,000,80,000,120,000,140,000 iterations are as follows: the mAP values are 0.5476, 0.5570, 0.5798, 0.5845, 0.5826, respectively, while the results of adding samples by random method are 0.5067, 0.5502, 0.5670, 0.5720, and 0.5770. The results of the two methods seem to be the same.My pretrained model is resnet-101.