6 Commits

Include renames

Author	SHA1	Message	Date
GotthardGandClaude Fable 5.1	82f0d9099b	gate: bootstrap by mount, light states and excluded frames of the gold set ... Frames of one mount are the same loop seen again, so the paired bootstrap now draws mounts (data/gold/groups.json, built by tools/gold_groups.py from the mount ids the studio writes in the gold manifest). The comparison also reports the unseen core without the saturated frames, and frames the studio dropped from the gold set are pruned. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>	2026-10-02 15:46:31 +02:00
GotthardGandClaude Fable 5.1	62f568b0f6	gate: baseline and candidate measured in the same environment ... The cluster's first run was gated against a baseline measured on the Spark. The same weights score lower there (F1 0.840 vs 0.861, mask score 0.706 vs 0.757 on the 203 gold frames), so the gate failed a model that beats the active one on F1 in 98.7 % of bootstrap draws when both are measured on one machine. Gold reports now record the environment (ultralytics, torch, host). When the active model's report comes from other versions, train_run.py measures its weights again in the job and gates against that; the predictions feed the paired bootstrap. Without the weights there is no verdict and no promotion. The SLURM scripts pull the active model's weights. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>	2026-10-02 13:25:45 +02:00
GotthardGandClaude Fable 5.1	831367d35d	noise-aware gate, frozen core of unseen frames, and a cycle script for the Spark ... A paired bootstrap over the 149 unseen gold frames (2026-10-02) showed that a difference between two models carries about +-0.023 of noise in F1, +-0.018 in the mask score and +-0.10 in crystal F1, while the gate's tolerance was 0.005. - tools/gate_stats.py: per-frame counts and a paired bootstrap (pure Python). - train_run.py: when the baseline's gold predictions exist, the gate requires the candidate to beat it on F1 or the mask score in 90 % of the draws and not to lose the other in more than 90 %; the point rule is the fallback and is always reported (pass_point_rule). - tools/compare_unseen.py replaces the Spark's ~/fair_gate_all.py: all models on the frames none trained on, on the frozen core (data/gold/unseen_core.json, 149 frames) and on all unseen frames once the gold set grows, with the interval and win share against the active model. - measure_weights.py keeps a version's gate verdict when it re-measures it. - tools/spark_cycle.sh: gold growth, export, re-measure, train, gate, compare, push, with what cycles 3 and 4 taught built in. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>	2026-10-02 13:25:45 +02:00
GotthardGandClaude Fable 5.1	3794330fc3	gate: mask term over all gold objects (unmatched = 0), so finding more objects is not punished; tests ... Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>	2026-09-28 19:16:01 +02:00
GotthardGandClaude Fable 5.1	4698dbeaf9	training loop: run ledger, studio export importer with an image pool, train_run with gold gate and auto-promotion, SLURM template, CI ... Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>	2026-09-28 16:06:06 +02:00
duan_j	391c357c84	migration and splitting AareLC	2026-04-14 16:07:31 +02:00