lightly-ai / lightly-ai/lightly-train
[QUESTION] Handling Unlabeled and Background Classes in Mask2Former for Custom Dataset
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Description
### 🤔 What’s your question?
I want to train a model on a dataset from https://arxiv.org/html/2507.16855v1, where the labels are defined as: class 0 for unlabeled pixels (due to uncertain categories), class 1 for background, and classes 2-16 for foreground. However, when I examined the Mask2Former loss code, it seems to treat the ignore_index and background as the same category. Does this mean all ignore_index pixels would be predicted as background? This appears incompatible with my dataset, where unlabeled (class 0) and background (class 1) are distinct. How should I handle these classes to ensure proper training with Mask2Former?
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Read the Mask2Former loss code and the dataset label definitions described in the issue. Verify how ignore_index and background are represented, then document the supported handling for class 0 (unlabeled), class 1 (background), and classes 2–16; done when the training behavior and required configuration are clear.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100