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.