Comments (1)
The boundary snapping gives a boost of around 2.5% on the IoU metric as shown in the paper. Therefore, it is a refinement of the appearance model (the one that this code reproduces) and not the core of the algorithm. The boundary snapping is implemented in Matlab and implementing it in python would require quite a lot of time so we are not planning to do it. If you would like to use it, you can try the caffe version.
In the paper we also tried another refinement using the Fast Bilateral Solver which also gives a nice (but smaller than boundary snapping) improvement and there is code available in python. Thus, I would recommend you to give it a try.
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Related Issues (20)
- Code HOT 1
- class_balanced_cross_entropy_loss HOT 1
- Confused by " inputs.requires_grad_()" HOT 3
- online training HOT 1
- Implement three measures HOT 3
- Why total iterations are different to them in the paper? HOT 1
- official measure code HOT 7
- How to add Mask Input? HOT 1
- Can you deliver a pretrain .pth model file of the final model HOT 1
- loss islarge HOT 3
- i can't find where you use finetuning on first frame in your code-pytorch HOT 5
- can you help me, how to use this code on Davis 2017 HOT 2
- train_online RuntimeError HOT 1
- Optimizer learning rates HOT 1
- Question about evaluation result HOT 1
- an error occurred when running train_parent.py HOT 6
- How to Evaluate the model? HOT 1
- Can it be used in images? HOT 1
- Emergency!
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