Model Evaluation
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Quiet
- Tech stack
- python
- Domain
- machine-learning, testing-qa
Research direction
Start by locating the FLUX.2 Inpainting + ControlNet training script and checking whether it already exposes validation or evaluation entry points. Review the surrounding training documentation and examples; done means documenting a concrete validation setup, metrics, and any early-stopping guidance supported by the project.
Written by the indexing model from the issue text.
Description
I have a question regarding model evaluation. I do not see any validation loss or validation metrics in the training script. What is the recommended way to determine whether the model is overfitting or underfitting during FLUX.2 Inpainting + ControlNet training? Is there a standard validation setup, evaluation metric, or early-stopping strategy that you recommend?
- Dominant language
- Python
- Stars
- 13.2k
- Forks
- 1.3k
- Avg merge
- 22h 13m
- Merged PRs (30d)
- 29
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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.
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