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Expose real train artifacts in dashboard

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admin
2026-06-30 23:38:03 +08:00
parent fb96c96d8b
commit 3e9e8ba6f5
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@@ -279,9 +279,11 @@ image/txt pair, generates `dataset.yaml`, runs one CPU epoch through
`yolo.train_custom`, verifies `results.csv` and `best.pt`, then uses that
trained checkpoint for prediction and GradCAM heatmap jobs. The latest report
is available from `GET /api/acceptance/real-train/latest` and is shown in the
coverage panel as `真实短训`. This is heavier than the real-data predict
acceptance, so run it when you want proof that real uploaded data can create
loss curves, trained weights, segmentation previews, and heatmap artifacts.
coverage panel as `真实短训`, including direct links to the generated `best.pt`,
`results.csv`, prediction preview, and heatmap images. This is heavier than
the real-data predict acceptance, so run it when you want proof that real
uploaded data can create loss curves, trained weights, segmentation previews,
and heatmap artifacts.
For stronger runtime proof, `POST /api/acceptance/deep` runs minimal training
loops for the three model families: one SegModel optimizer step, one YOLO