Comments (1)
You need to run some sort of clustering algorithm to get instance labels. Our metrics and loss only use x/y coordinates. You can check http://openaccess.thecvf.com/content_cvpr_2018/papers/Kendall_Multi-Task_Learning_Using_CVPR_2018_paper.pdf for details as they use OPTICS algorithm. Their implementation is here: https://github.com/alexgkendall/OPTICS_Clustering
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Related Issues (20)
- depth loss and metric
- Connection between fmp and task branches HOT 4
- IndexError: list index out of range (when reading base_path in "celeba_loader.py" line 52 )
- size of the segmentation output HOT 1
- run error, Please help me, Thanks HOT 1
- Question about the MinNormSolver
- Unable to obtain the same average error as Table 1 in the paper
- Unable to obtain the same average error as Table 1 in the paper HOT 1
- How to convert .pkl file to .onnx model. I mean there are two parts of the whole model. HOT 4
- Redundancy of optimiser.zero_grad()? HOT 2
- cityscape dataset
- About Cityscapes Instance Segmentation GroundTruth Generation.
- `iter_count` is never incremented in `min_norm_solvers`?
- is the code corresponding to the algorithm in paper ? HOT 1
- If I want to train the 3D image data using MTL by MGDA do I have to change the code min_norm_solver.py? HOT 1
- local variable 'sol' referenced before assignment HOT 1
- Algorithm 2 line 10 HOT 1
- Is there any difference between soft sharing and hard sharing network when using MGDA? HOT 1
- A question about the output of rep HOT 1
- params.json HOT 1
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