sto-warp 1.0.10

Star Trek Online screenshot recognition + ML training (standalone WARP/WARP CORE).

STO-WARP

Star Trek Online screenshot recognition + ML training, distributed as a standalone Python package.

What is this?

STO-WARP combines two tools in one package:

WARP (Weaponry & Armament Recognition Platform) — reads your in-game screenshots and automatically fills in your SETS build. Detects equipment, traits, bridge officers, and ship information using computer vision and machine learning.

WARP CORE — trainer interface built into WARP. Review and correct recognition results, confirm annotations, and retrain the local ML models (Icon Classifier + Layout Regressor) on your own data to improve accuracy over time.

Technology

• WARP — recognition pipeline (OCR + layout detection + icon matching). Reads STO screenshots and emits structured slot/item results.
• WARP CORE — Qt trainer UI for reviewing recognition output and fine-tuning the EfficientNet / MobileNetV3 models that drive WARP.

Install

The recommended way to install sto-warp is via our universal installation scripts, which will automatically configure an isolated environment using pipx.

Linux / macOS:

curl -sSL https://raw.githubusercontent.com/raman78/sto-warp/main/install.sh | bash

Windows (PowerShell):

Invoke-WebRequest -Uri "https://raw.githubusercontent.com/raman78/sto-warp/main/install.ps1" -OutFile "install.ps1"; .\install.ps1; Remove-Item "install.ps1"

For manual installation methods and more details, see INSTALLATION.md.

Data and models

On first run sto-warp downloads:

• ML models (icon_classifier.pt, screen_classifier.pt) from the Hugging Face hub.
• Cargo / ship / trait JSON from the community STOCD/SETS-Data repository.

Everything is cached under ~/.config/warp/ (or $XDG_CONFIG_HOME/warp/ when set). Nothing is committed to this repository.

License

GPL-3.0 — see LICENSE.

Project docs

• INSTALLATION.md — install methods (pipx, native packages).
• CHANGELOG.md — release notes.
• PROJECT_CONTEXT.md — design context.
• CLAUDE.md — repository rules for AI-assisted development.
• docs/ — technical deep-dives (BOFF / trait detection, ML pipeline, …).