Devlog ·

Vision Classifier Worklog — March 29, 2026

~1 min read · 336 words

Date:

  • 2026-03-29

Worktree:

  • /tmp/jc_reborn_ps1_debug

Branch:

  • worktree-ps1-debug-20260329
On this page

Goal

Build a local CPU-only visual classifier pipeline that:

  • uses the canonical reference scenes as source of truth
  • runs under low-memory constraints
  • produces structured semantic artifacts
  • generates human-reviewable HTML pages
  • scales across all 63 reference scenes

Constraints

  • effectively no usable GPU
  • about 8 GB RAM
  • avoid heavyweight runtime dependencies
  • acceptable to trade speed for robustness

Decisions

The pipeline is reference-first, not training-first.

It uses:

  • deterministic visual features
  • lightweight per-frame feature vectors
  • nearest-reference retrieval against the canonical bank
  • semantic summaries derived from reference-bank matches

It deliberately does not depend on:

  • torch
  • transformers
  • sklearn

Current runtime dependencies are:

  • Pillow
  • NumPy

Implemented

Primary code:

  • /tmp/jc_reborn_ps1_debug/scripts/vision_classifier.py
  • /tmp/jc_reborn_ps1_debug/scripts/run-vision-reference-pipeline.sh
  • /tmp/jc_reborn_ps1_debug/scripts/publish-vision-pipeline.py

Design doc:

  • /tmp/jc_reborn_ps1_debug/docs/ps1/research/LOCAL_VISION_CLASSIFIER_PLAN_2026-03-29.md

Completed Outputs

Reference bank:

  • /tmp/jc_reborn_ps1_debug/artifacts/vision-reference-bank-20260329/

Reference self-check, latest:

  • repo:/vision-artifacts/vision-reference-selfcheck-20260329-v4/

Published top-level entry:

  • repo:/vision-artifacts/vision-reference-pipeline-current/index.html

Artifact Types Now Available

For all 63 reference scenes:

  • per-scene review.html
  • per-scene vision-analysis.json
  • bank-level index.html
  • self-check index.html
  • quality report
  • confusion report
  • family report
  • inventory JSON
  • inventory HTML
  • inventory CSV
  • strongest-scenes JSON
  • weakest-scenes JSON
  • top-level manifest JSON

Scale Reached

Reference scenes processed:

  • 63

Reference frames indexed:

  • 13,128

Key Improvements Made

  1. Built a full bank over all reference frames.
  2. Added per-run analysis against that bank.
  3. Added per-scene HTML review outputs.
  4. Added full self-check over all 63 references.
  5. Tightened label rules to reduce excessive ocean classification.
  6. Added quality, confusion, and family-level reports.
  7. Added export-friendly inventory artifacts and top-level publishing.

Current Limits

The pipeline is operational, but semantic quality is still v1.

Known weak areas:

  • similar FISHING scenes cross-match each other
  • several STAND scenes remain hard to separate
  • some scene families still need stronger actor/sprite semantics

So:

  • pipeline completeness: good
  • artifact completeness: good
  • semantic precision: improving, not final

Next Practical Step

Run this exact pipeline against PS1 result directories and produce:

  • PS1 review.html
  • PS1 vision-analysis.json
  • PS1 quality summary against the reference bank
  • scene ranking by semantic mismatch mode

That is the next step that will directly help fix PS1 scene bugs.