num_classes in segmentation examle
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 17.9k
- Forks
- 7.3k
- Avg merge
- 1d 15h
- Merged PRs (30d)
- 13
Description
Hi, I have some questions about num_classes in the segmentation example.
Since the pretraining classes is 21, should the target mask be integers from 0 to 21, where 0 is the background?
If so, my next question is do we need to compute loss over 0 (background)? I think generally background pixels is the majority, will it hurt the model so that the model would always predict 0 (background) as the majority? Or should we ignore background label by passing ignore_index=0 in cross entropy loss? I see that here we are ignoring index 255, but I am not sure where 255 comes from.
Thanks! Any input is highly appreciated!
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the references/segmentation example and references/segmentation/train.py at the ignore_index=255 usage. Trace how num_classes, target masks, and the loss are defined, then compare the example's behavior with the linked pretrained segmentation classes. Done means the example clearly documents the class and background-label conventions and explains the source of 255.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100