jungfrau and Claude Fable 5.1
8c3c538244
gold: 285 frames (batch 8 added by mount: 83 corrected frames, 2-per-mount cap, 20 siblings held out); every model re-measured
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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 >
2026-10-05 15:49:30 +02:00
jungfrau and Claude Fable 5.1
c7d53646ce
small cluster model re-gated on the Spark in one environment: gate_passed against v3; comparison after cycle 4
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Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com >
2026-10-02 13:30:10 +02:00
jungfrau
f37604f87a
run 2026-10-01_v4-noflip-mask1: promoted; comparison updated
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2026-10-02 13:25:46 +02:00
jungfrau
a67e6b8e8e
run 2026-10-01_v4-recipeA: gate_failed; comparison updated
2026-10-02 13:25:45 +02:00
Duan
a9192406d3
yolo26s-seg-v3-s-640px-2026-10-01
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2026-10-02 10:47:39 +02:00
jungfrau
2956303ffe
run 2026-09-29_v3-noflip-mask1: promoted; comparison updated
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2026-09-30 13:56:07 +02:00
jungfrau and Claude Fable 5.1
f1a945528e
cluster runs on dataset v3: resolution and model-size levers
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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 >
2026-09-30 07:18:06 +02:00
jungfrau
d562b94671
run 2026-09-29_v3-recipeA: gate_passed; comparison updated
2026-09-29 23:42:41 +02:00
jungfrau
0ec5138409
all models re-measured on the 203-frame gold set; comparison on the unseen frames updated
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2026-09-29 18:00:50 +02:00
jungfrau
3d39d40560
run 2026-09-29_v2-recipeA: gate_failed; comparison updated
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2026-09-29 17:58:39 +02:00
jungfrau
0742281af0
baseline: yolo26n-seg-alc-v1-noflip-mask1-2026-09-29 re-measured on the gold set grown to 203 frames
2026-09-29 17:58:39 +02:00
jungfrau
a6efca1c10
base weights yolo26n-seg.pt under LFS, so a cluster clone needs no download
2026-09-29 17:58:39 +02:00
jungfrau
e7a02cd1ea
run 2026-09-29_alc-v1-noflip-mask1: promoted (same detection as A, tightest masks); loop switches to it
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2026-09-29 11:13:44 +02:00
jungfrau
703bfc2e11
run 2026-09-29_alc-v1-noflip-mask1: gate_passed, comparison updated
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2026-09-29 10:37:36 +02:00
jungfrau
40dc9e54e3
run 2026-09-28_alc-v1-800px: gate_failed
2026-09-29 00:31:02 +02:00
jungfrau
13f4d6e3f7
run 2026-09-28_alc-v1-mask1: gate_failed
2026-09-28 23:54:53 +02:00
jungfrau
e84bcfb658
run 2026-09-28_alc-v1-noflip: gate_passed
2026-09-28 21:30:31 +02:00
jungfrau
ed208bff95
run 2026-09-28_alc-v1-humanonly: gate passed, not promoted (A/B on clean gold: masks no tighter without accepted-unchanged labels, crystal worse)
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2026-09-28 20:44:27 +02:00
jungfrau
7bfd799c26
run 2026-09-28_alc-v1: promoted by the gate after the mask-term fix (F1 +0.045, mask score 0.678 -> 0.710)
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2026-09-28 19:18:27 +02:00
jungfrau
e8475684d4
run 2026-09-28_alc-v1: gate_failed (F1 +0.045, mask IoU -0.022 vs the deployed model)
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2026-09-28 19:08:07 +02:00
jungfrau
71ee34cd17
baseline: gold measure of the deployed yolo26n-seg-overlap-false-2026-04-12
2026-09-28 16:51:45 +02:00
GotthardG and Claude 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
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Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com >
2026-09-28 16:06:06 +02:00
duan_j
4fba05631e
track base models with git lfs
2026-04-14 16:15:10 +02:00
duan_j
391c357c84
migration and splitting AareLC
2026-04-14 16:07:31 +02:00
duan_j