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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

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

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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