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Validate uploaded YOLO custom workflow

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admin
2026-06-30 15:17:50 +08:00
parent 777f168a75
commit 826027629b
3 changed files with 77 additions and 5 deletions
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README.md
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@@ -78,10 +78,13 @@ heatmap/segmentation artifacts, training curves, and weight manifest status.
The same panel can run `POST /api/acceptance/smoke`, a lightweight live smoke
that creates an upload dataset, uploads a label, downloads it through the
artifact API, runs a mock job, checks SSE log streaming, and executes one
legacy image/label overlay job on tiny generated PNGs. It also runs model
family readiness checks: a SegModel/SMP forward pass, a YOLO segmentation
prediction on a tiny image, MMSeg config parsing, and local MMSeg pretrained
weight discovery. MMSeg full-model readiness is validated in
legacy image/label overlay job on tiny generated PNGs. It also launches
`yolo.predict_custom` and `yolo.heatmap_custom` through the normal job queue
against the uploaded sample image, proving that upload datasets can produce
browsable segmentation and heatmap artifacts. It also runs model family
readiness checks: a SegModel/SMP forward pass, a YOLO segmentation prediction
on a tiny image, MMSeg config parsing, and local MMSeg pretrained weight
discovery. MMSeg full-model readiness is validated in
`SEG_MMSEG_CONDA_ENV` by importing `mmcv._ext` and building a local MMSeg
`EncoderDecoder` from the existing config tree.