About the trained model.

[ddghost]

Hi, when I use your trained model for clipart. The model can not find these parameters:

'RCNN_base2.0.2.bn2.num_batches_tracked', 'RCNN_base3.0.5.bn2.num_batches_tracked', 'RCNN_base1.4.0.bn3.num_batches_tracked', 'RCNN_base3.0.6.bn1.num_batches_tracked', 'RCNN_base3.0.18.bn2.num_batches_tracked', 'RCNN_base3.0.4.bn1.num_batches_tracked', 'RCNN_base3.0.12.bn3.num_batches_tracked', 'RCNN_base3.0.16.bn3.num_batches_tracked', 'RCNN_base3.0.9.bn2.num_batches_tracked', 'RCNN_base1.4.0.downsample.1.num_batches_tracked', 'RCNN_base2.0.0.bn3.num_batches_tracked', 'RCNN_base1.4.2.bn3.num_batches_tracked', 'RCNN_base3.0.13.bn1.num_batches_tracked', 'RCNN_base3.0.20.bn2.num_batches_tracked', 'RCNN_base3.0.4.bn2.num_batches_tracked', 'RCNN_base1.4.0.bn1.num_batches_tracked', 'RCNN_base3.0.13.bn2.num_batches_tracked', 'RCNN_top.0.0.downsample.1.num_batches_tracked', 'RCNN_base3.0.12.bn1.num_batches_tracked', 'RCNN_base3.0.20.bn3.num_batches_tracked', 'RCNN_base3.0.3.bn3.num_batches_tracked', 'RCNN_base3.0.1.bn2.num_batches_tracked', 'RCNN_base3.0.5.bn1.num_batches_tracked', 'RCNN_base1.1.num_batches_tracked', 'netD3.bn1.num_batches_tracked', 'RCNN_base3.0.16.bn2.num_batches_tracked', 'RCNN_base3.0.15.bn2.num_batches_tracked', 'RCNN_base3.0.21.bn1.num_batches_tracked', 'RCNN_base3.0.18.bn3.num_batches_tracked', 'RCNN_base3.0.8.bn1.num_batches_tracked', 'RCNN_base2.0.0.downsample.1.num_batches_tracked', 'netD_inst.bn2.num_batches_tracked', 'RCNN_base3.0.21.bn2.num_batches_tracked', 'RCNN_top.0.1.bn1.num_batches_tracked', 'RCNN_base3.0.21.bn3.num_batches_tracked', 'RCNN_base3.0.1.bn1.num_batches_tracked', 'RCNN_base3.0.0.bn2.num_batches_tracked', 'RCNN_base2.0.3.bn2.num_batches_tracked', 'RCNN_base3.0.1.bn3.num_batches_tracked', 'RCNN_base2.0.0.bn1.num_batches_tracked', 'RCNN_base3.0.14.bn2.num_batches_tracked', 'RCNN_base3.0.17.bn1.num_batches_tracked', 'RCNN_base3.0.20.bn1.num_batches_tracked', 'RCNN_base3.0.2.bn3.num_batches_tracked', 'RCNN_base2.0.2.bn1.num_batches_tracked', 'RCNN_base3.0.7.bn1.num_batches_tracked', 'RCNN_base3.0.22.bn1.num_batches_tracked', 'RCNN_base2.0.2.bn3.num_batches_tracked', 'RCNN_base3.0.11.bn3.num_batches_tracked', 'RCNN_top.0.2.bn1.num_batches_tracked', 'RCNN_base1.4.2.bn1.num_batches_tracked', 'RCNN_top.0.1.bn3.num_batches_tracked', 'RCNN_base3.0.19.bn2.num_batches_tracked', 'RCNN_base2.0.1.bn1.num_batches_tracked', 'RCNN_base3.0.7.bn2.num_batches_tracked', 'RCNN_top.0.2.bn3.num_batches_tracked', 'RCNN_top.0.1.bn2.num_batches_tracked', 'RCNN_base1.4.0.bn2.num_batches_tracked', 'RCNN_base1.4.1.bn3.num_batches_tracked', 'RCNN_base3.0.10.bn1.num_batches_tracked', 'RCNN_base3.0.19.bn3.num_batches_tracked', 'RCNN_base3.0.17.bn3.num_batches_tracked', 'RCNN_base3.0.11.bn2.num_batches_tracked', 'RCNN_base3.0.19.bn1.num_batches_tracked', 'RCNN_base3.0.8.bn3.num_batches_tracked', 'RCNN_base3.0.14.bn1.num_batches_tracked', 'RCNN_base3.0.12.bn2.num_batches_tracked', 'RCNN_base1.4.2.bn2.num_batches_tracked', 'RCNN_base3.0.11.bn1.num_batches_tracked', 'RCNN_base1.4.1.bn2.num_batches_tracked', 'RCNN_base2.0.0.bn2.num_batches_tracked', 'RCNN_base3.0.6.bn2.num_batches_tracked', 'RCNN_base3.0.10.bn2.num_batches_tracked', 'RCNN_base3.0.6.bn3.num_batches_tracked', 'RCNN_base2.0.3.bn1.num_batches_tracked', 'RCNN_base3.0.9.bn3.num_batches_tracked', 'netD2.bn1.num_batches_tracked', 'RCNN_base3.0.2.bn1.num_batches_tracked', 'RCNN_base2.0.3.bn3.num_batches_tracked', 'RCNN_base3.0.0.downsample.1.num_batches_tracked', 'RCNN_base3.0.22.bn3.num_batches_tracked', 'RCNN_top.0.0.bn1.num_batches_tracked', 'RCNN_base3.0.4.bn3.num_batches_tracked', 'RCNN_top.0.0.bn2.num_batches_tracked', 'RCNN_base1.4.1.bn1.num_batches_tracked', 'RCNN_base3.0.10.bn3.num_batches_tracked', 'RCNN_base3.0.3.bn2.num_batches_tracked', 'netD2.bn2.num_batches_tracked', 'RCNN_base3.0.5.bn3.num_batches_tracked', 'RCNN_base3.0.3.bn1.num_batches_tracked', 'RCNN_base3.0.7.bn3.num_batches_tracked', 'RCNN_base3.0.22.bn2.num_batches_tracked', 'RCNN_base3.0.18.bn1.num_batches_tracked', 'netD3.bn3.num_batches_tracked', 'RCNN_base3.0.17.bn2.num_batches_tracked', 'RCNN_base3.0.2.bn2.num_batches_tracked', 'RCNN_base3.0.15.bn3.num_batches_tracked', 'RCNN_base3.0.0.bn1.num_batches_tracked', 'RCNN_base3.0.8.bn2.num_batches_tracked', 'RCNN_base3.0.13.bn3.num_batches_tracked', 'RCNN_base2.0.1.bn2.num_batches_tracked', 'netD2.bn3.num_batches_tracked', 'RCNN_top.0.0.bn3.num_batches_tracked', 'RCNN_base3.0.0.bn3.num_batches_tracked', 'RCNN_top.0.2.bn2.num_batches_tracked', 'RCNN_base3.0.14.bn3.num_batches_tracked', 'netD3.bn2.num_batches_tracked', 'RCNN_base2.0.1.bn3.num_batches_tracked', 'RCNN_base3.0.16.bn1.num_batches_tracked', 'RCNN_base3.0.15.bn1.num_batches_tracked', 'RCNN_base3.0.9.bn1.num_batches_tracked'

When I ignore this error and test, the mAP is only 40.4. How to get the correct mAP?

AP for aeroplane = 0.3302
AP for bicycle = 0.4909
AP for bird = 0.3590
AP for boat = 0.2591
AP for bottle = 0.3835
AP for bus = 0.5573
AP for car = 0.3868
AP for cat = 0.1590
AP for chair = 0.3883
AP for cow = 0.5839
AP for diningtable = 0.1884
AP for dog = 0.2369
AP for horse = 0.3690
AP for motorbike = 0.6995
AP for person = 0.6064
AP for pottedplant = 0.4975
AP for sheep = 0.2572
AP for sofa = 0.3483
AP for train = 0.4715
AP for tvmonitor = 0.5138
Mean AP = 0.4043

[harsh-99]

Hey,
It might be the case that by mistake I have uploaded the checkpoint of pretrained model of earlier codebase (which was then cleaned and made public). An easy and quick way out would be to compare the current model dictionary with the save dictionary (model) and modify the saved dictionary (model) as per the variable. I will also try to correct them.