--skip-train registers the version with the day it runs, so the nano and the yolo26s of
cycle 5 existed twice in the registry (2026-10-23 and 2026-10-25); the runs point at the
10-25 ones, the others are removed. A run whose base weights are a registry version (the
warm start) was named after the file weights.pt; it is now named after that version's family.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013uu3Bxsu5JqFhaWa9Ftu6z
454 of the 1 029 gold outlines are a proposal kept unchanged (loop 159 of 317, pin 140 of
224, face 140 of 295, crystal 14 of 149): the pre-labelling model's own masks. The
evaluation now ignores them object by object for every model, like a crowd region: they
leave the gold, and an unmatched prediction on one of them is not a false alarm.
tools/gold_kept.py finds them against the studio's batch proposals (mask IoU >= 0.98) and
writes data/gold/kept_machine.json; evaluate_image, evaluate_dirs and gate_stats.per_frame
read it. With it, the frame-level exclusion of 845bd87 is no longer applied (it stays as
the fallback without the file), so the gold set can grow with frames the active model
pre-labelled. Every registry model is measured again on this basis and the three v5 runs
are re-gated, without promotion.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013uu3Bxsu5JqFhaWa9Ftu6z
Where a reviewer keeps an outline the model proposed, the gold holds the model's own mask,
which overlaps itself perfectly: on the 50 gold frames v4 had pre-labelled it scored a
matched IoU of 0.959 against 0.905 elsewhere, every other model 0.93, and that alone
produced the "mask dip" that failed all three v5 runs. tools/gold_prelabel.py records
which model proposed each gold frame (data/gold/prelabel.json, 265 of 335 frames);
gate_stats.without_own_proposals drops those frames from that model's measure; the unseen
comparison and the gate's paired bootstrap use it, so two models are compared on the
frames neither of them touched. The three v5 runs are re-gated on that basis, without
promotion.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
231 frames unseen by every model (core 148), 82 crystals on them. v4-noflip-mask1 stays
the best: F1 0.855, mask score 0.798; v3-noflip-mask1 0.848 / 0.793, within noise;
the small cluster model now clearly loses on masks (-0.021, won in 0 % of draws).
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Three ledger runs for the cluster, exported on dataset/2026-09-29_v3 with the
promoted recipe: nano at 1024 px, small at 640 px, small at 1024 px. A run can
now carry its own training arguments (train_args in run.json, set with
ledger new --kv), which train_run.py applies under any --set; train.sbatch
forwards extra arguments after the run id (e.g. --batch 8). yolo26s-seg.pt
joins the base weights under LFS. slurm/README lists the runs, the questions
they answer and the commands.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
duan_j