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Our Notes BoxLens v0.1.0

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@nichinichisou0609 nichinichisou0609 released this 27 Sep 18:15
· 16 commits to main since this release
v0.1.0
2ea25d8

Our Notes BoxLens v0.1.0 is the first public release. Source code and model weights use MIT; third-party notices are retained. The repository and attachments contain no game client assets, screenshots, fonts, master-data exports, or ready-made card gallery.

Included

  • Source archive and Python CPU wheel.
  • Encoder and field model weights in ONNX and PyTorch formats, plus verified training initializers and hash manifests.
  • Training recipe, frozen code required to trace the released checkpoint lineage, schemas, evaluation summaries, and CPU benchmarks.

Evaluation

  • Clear card-rank matrix: all 122 catalog identities at ranks 1–5, across four resolutions; 2,440 observations, 2,440 identities and ranks correct. This isolates rank and fixes level to Lv.1. Rank is read by sprite-template matching, not a separate neural classifier.
  • Training coverage: scene data contains all levels 1–90; numeric field crops cover 1–100. The five rank counts are 7,208 / 7,089 / 7,371 / 7,357 / 7,372.
  • Independent synthetic full-screen tests: 560 normal and 280 stress screenshots across five languages and multiple parameter views. Emitted field values had zero errors in these sets, while rejected and unlocalized fields remain; the stress set has lower coverage. Detailed counts and strata are in the repository reports.
  • Real-sample regression: ten screenshots from one collection gave 156/156 identities, 20/21 readable levels, 120/120 visible ranks, and 18/18 training counts. This is calibration data, not a representative real-world test.
  • CPU scan benchmark: on Windows 10 / Intel i5-12400F, median 1.82 s with two logical CPUs and 1.66 s with four for ten images; P95 was 2.43 s and 2.90 s respectively. Other machine load was not controlled.

Clear, fully visible list screenshots are the supported target. Occlusion, overlays, fade effects, and heavy compression can cause omissions or misreads; reposition and recapture, then review or manually correct uncertain values. Synthetic and single-user regression results do not establish general accuracy across players, devices, or future client versions.

Verify the attached source, weights, and wheel using SHA256SUMS.txt.

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