Contributing Models

PowerAim’s in-app Downloadable Models tab pulls from fgilde/AI-Ming plus a configured upstream community model repo and merges them transparently. If the same filename exists in both, the newer commit wins; on a tie, PowerAim’s repo wins. Users see one combined list.

Naming convention

If you’d like to be credited, name your model:

[Game Name] by [Your Handle].onnx

If you’d rather stay anonymous:

[Game Name].onnx

How to upload

Submit to PowerAim’s repo:

Fork https://github.com/fgilde/AI-Ming
Drop your .onnx into the models/ folder of your fork
Open a Pull Request against main
Once merged, your model appears in everyone’s in-app Downloadable Models tab on next refresh

Quality bar

We don’t reject models — anyone can submit. But for your model to be useful:

Train on varied data (different maps, weapons, lighting, view angles)
Export with dynamic input axis (dynamic=True)
Include a names metadata field for multi-class models
Test in-game first — open PowerAim, enable Show Detected Player, and verify confidences are reasonable

See Training Your Own for the full pipeline.

Removing a model

If you want a model removed, open another PR removing the file, or open an Issue against the repo where it lives.

Tips for high-quality models

YOLOv8 architecture exported to ONNX with NHWC → NCHW input order
Input image size from 192 to 1280 (multiple of 32) — PowerAim picks this up automatically from the ONNX metadata
Multi-class supported — names custom metadata field with Ultralytics convention (e.g. {0: "Enemy", 1: "Teammate"})
Thousands of varied images — PowerAim’s detection quality is bottlenecked by training data, not by inference
Test in-game with Show Detected Player enabled — verify class confidences are reasonable

Thank you for contributing! PowerAim and the wider community benefit massively from shared models.