Comments (3)
Did you switch all the batch norm to eval mode? I received the report the model could be trained on Pascal VOC 2012, although the result was 1 point lower (here). Basically, I think you only have to replace the COCO-Stuff data loader with a third-party one for VOC. Anyway, please try with a default setting (10 samples as a batch, 20k steps)
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Thank you for your reply. It works really well. I have another question, the init model, deeplabv2_resnet101_VOC2012.pth, is it trained on resnet for image classification or something?
from deeplab-pytorch.
An init model is actually deeplabv2_resnet101_COCO_init.pth
, which is "ResNet-101 + ASPP" model trained on MSCOCO segmentation set. The "ResNet" part was pre-trained on ImageNet.
The deeplabv2_resnet101_VOC2012.pth
that you mentioned is a model which was initialized with MSCOCO parameters above and trained on Pascal VOC 2012.
from deeplab-pytorch.
Related Issues (20)
- only 20% mIou HOT 4
- The results of VOC2012 val dataset HOT 2
- cannot find setup_voc12.sh
- how to do the augmentation for training? HOT 2
- what do we mean by unary? HOT 2
- custom dataset HOT 2
- get_dataset HOT 2
- the pre-trained model HOT 2
- The training result is poor after using the weak supervision data set HOT 4
- How to convert the generated npy file into an image HOT 4
- A simple question about test on VOC HOT 1
- COCO number of classes HOT 4
- Question VOC HOT 1
- How to train on COCO2014 dataset HOT 1
- How to train on the coco-stuff datasets? HOT 1
- Training Deeplab-pytorch on VOC2012 raises metaclass conflict error with TensorFlow 1.10.0 and ANTLR downgrade to 4.8
- pascal voc2012 dataset HOT 1
- Big loss
- Extract Feature map HOT 2
- How to refer to your work? HOT 1
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from deeplab-pytorch.