Inclusive Yogi XL Model Training

I have the base Yogi XL model running in my pipeline, but its performance drops whenever the user falls outside the narrow, “average” profile it was originally tuned on. I want to retrain it so that race, age, and body type are all learned equally well, producing a genuinely universal model that works for men, women, and trans users of every demographic and have a good support of ipAdaptor (or train an ipadator version)

You will need to supply a large, well-labeled data set that represents the full spread of human diversity. The source must be legally usable for commercial AI work;You will handle data cleaning, augmentation, and the complete fine-tuning process—then return the updated weights along with inference scripts and a short technical report.

Deliverables
• Curated, diversity-balanced training set (or verifiable linkage to it) I will also send you a reference site.
• Fine-tuned Yogi XL model weights
• Validation metrics showing consistent accuracy across race, age, and body type cohorts
• Brief guide for integrating the new model back into my existing pipeline

Acceptance will be based on a side-by-side test: the new model must match or exceed baseline accuracy overall while closing the performance gap between demographic groups to within 2 %. If you already have experience building inclusive vision or pose-analysis networks—and, crucially, you can bring the data—let’s talk.