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The model and loaded state dict do not match exactly #35
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Can you please provide the command you are running and the full log output? |
20240814_092946.log |
And my command is 'python tools/train.py configs/tr3d/tr3d-ff_sunrgbd-3d-10class.py' |
But looks like it it not an error just warning and the metrics are fine? We load 2d backbone from imvotenet checkpoint, and this warning is about the extra head layers and missing 3d layers. |
But my [email protected] is only 0.6859 and [email protected] is only 0.5251. It's lower than you metrics .Your best metrics is that [email protected] is 69.4 and [email protected] is 53.4. how can I achieve your best metrics? |
In the paper we say that average mAP50 is 52.4, so to achieve 53.4 just run the same training for 5 times. |
Why do you need to use imvotenet's pre trained model? when I don't use the imvotenet's pre trained model.It's metrics is very low . [email protected] is only 0.6558 and [email protected] is only 0.4871. here is the complete log without pre trained models. |
I think we do it, because resnet50 from imvotenet is already pre-trained on sunrgbd. Starting training with image backbone initialized with random values is generally not a good idea. Also we for some reason freeze some resnet50 layers in these 3 lines. If you start with your own image backbone you probably should unfreeze these layers, and also don't forget to update image normalization here. |
i think so. just +- 1% randomness between training runs |
Should be with minimal randomness because of sampling 100k points. |
Thank you for your detailed reply. So is my trainning result normal? 51.5 is a little bit too random |
When I run with one GPU, it shows that the model does not match。
here is the log
unexpected key in source state_dict: img_rpn_head.rpn_conv.weight, img_rpn_head.rpn_conv.bias, img_rpn_head.rpn_cls.weight, img_rpn_head.rpn_cls.bias, img_rpn_head.rpn_reg.weight, img_rpn_head.rpn_reg.bias, img_roi_head.bbox_head.fc_cls.weight, img_roi_head.bbox_head.fc_cls.bias, img_roi_head.bbox_head.fc_reg.weight, img_roi_head.bbox_head.fc_reg.bias, img_roi_head.bbox_head.shared_fcs.0.weight, img_roi_head.bbox_head.shared_fcs.0.bias, img_roi_head.bbox_head.shared_fcs.1.weight, img_roi_head.bbox_head.shared_fcs.1.bias
missing keys in source state_dict: backbone.conv1.kernel, backbone.norm1.bn.weight, backbone.norm1.bn.bias, backbone.norm1.bn.running_mean, backbone.norm1.bn.running_var, backbone.layer1.0.conv1.kernel, backbone.layer1.0.norm1.bn.weight, backbone.layer1.0.norm1.bn.bias, backbone.layer1.0.norm1.bn.running_mean, backbone.layer1.0.norm1.bn.running_var, backbone.layer1.0.conv2.kernel, backbone.layer1.0.norm2.bn.weight, backbone.layer1.0.norm2.bn.bias, backbone.layer1.0.norm2.bn.running_mean, backbone.layer1.0.norm2.bn.running_var, backbone.layer1.0.downsample.0.kernel, backbone.layer1.0.downsample.1.bn.weight, backbone.layer1.0.downsample.1.bn.bias, backbone.layer1.0.downsample.1.bn.running_mean, backbone.layer1.0.downsample.1.bn.running_var, backbone.layer1.1.conv1.kernel, backbone.layer1.1.norm1.bn.weight, backbone.layer1.1.norm1.bn.bias, backbone.layer1.1.norm1.bn.running_mean, backbone.layer1.1.norm1.bn.running_var, backbone.layer1.1.conv2.kernel, backbone.layer1.1.norm2.bn.weight, 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head.cls_conv.bias, conv.0.kernel, conv.1.bn.weight, conv.1.bn.bias, conv.1.bn.running_mean, conv.1.bn.running_var
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