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Live training
Canonical source: versioned documentation. This wiki copy is maintained manually.
Start the local, read-only monitor from the repository's installed environment:
uv run --locked --no-sync pvextent monitor --repo-dir . --port 59654Open the printed http://127.0.0.1:59654/ address. The page refreshes every
two seconds and shows the current 1,000-photo mask proposal and independent
check counts. It prefers the current V4 quality-quarantine report and otherwise
reports the initial data/pvextent-training-v3 preparation.
Starting the monitor does not start training.
--no-sync also avoids changing optional dependencies while another process
is training or reviewing data. Prepare the project environment first using
the development setup or baseline guide.
The default Live / nächster Lauf view selects an active baseline run when one exists. Otherwise it explicitly reports that no training is active. Completed runs can be selected separately; their history is labelled as completed. Existing runs without batch telemetry show their recorded epoch losses and validation metrics, rather than reconstructed batch values.
New baseline runs append flushed scalar events to telemetry.jsonl during
training, including each real optimization batch, completed epoch, run
completion and handled failure. The monitor reads these events in the staging
directory while a run is active, then reads the published directory after
completion. It shows training loss by optimizer step and internal validation
IoU/Dice by epoch. The logger does not change model updates, sampling or RNG
state. Exact CPU continuation remains covered by the baseline regression tests.
The server binds only to loopback. Its fixed UI and /status.json endpoint
read bounded local JSON logs and reports; they do not load imagery, masks,
checkpoints or model libraries. The page has no training controls or external
services. The browser must stay on this computer; the localhost link is not
a published dashboard. Stop the monitor process to close the service.
Internal validation scores remain development measurements, with the limitations recorded in the training audit. The monitor does not evaluate the original test set or external pilot.
Developed and maintained by Valeford under the Spacement brand. Original project code: Apache-2.0. See the repository NOTICE for attribution.
Spacement PVExtent
- Home
- PVExtent Vidda vs. Tinden (v1.0 Releases)
- Getting started
- Dataset format
- Annotation guide
- PV4GER-SEG import
- Import audit
- Geographic splits
- Split audit
- Label review
- NRW candidates
- Bulk NRW training imagery
- Bulk mask curation
- Live training monitor
- Training quality exclusions
- Final bulk mask audit
- Experimental negative curation
- Negative curation audit
- Exploratory negative holdout
- Negative holdout audit
- Exploratory positive holdout
- Positive holdout audit
- Training smoke test
- Smoke audit
- Pretrained baseline training
- Baseline comparison
- Baseline comparison audit
- Six-epoch continuation audit
- V4 bulk training audit
- Domain-balanced training audit
- DeepLabV3+ ASPP architecture audit
- DeepLabV3+ Focal + Lovász loss audit
- DeepLabV3+ V5 Photometric Invariance audit
- CAD vectorization & rectilinear audit
- Test-Time Augmentation (TTA) audit
- Probability calibration & temperature scaling audit
- Seamless overlapping tiled inference & Hann blending audit
- Heterogeneous ensemble & quality triage audit
- Sealed 421 test benchmark audit
- 3D elevation & solar yield audit
- Multi-temporal 4D change detection audit
- Multispectral NIR & spectral signature audit
- Encoder weights and licenses
- Baseline audit
- Frozen validation errors
- Validation error audit
- Candidate predictions
- Research roadmap
Project