---

name: training description: "Configure EdgeConnect training, internal validation sampling, checkpoint resume, and loss behavior." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Training

Use this sub-skill when you need to plan, configure, or reason about EdgeConnect training-time stages and the internal validation loop.

Covers

• train.py as the thin MODE=1 wrapper over main.load_config(mode=1).
• The distinction between MODE (run type) and MODEL (stage family).
• EdgeConnect.train, EdgeConnect.eval, and EdgeConnect.sample behavior.
• Stage-specific checkpoint loading, saving, sampling, and logging.
• Generator/discriminator architecture, VGG-based losses, and internal metrics.

Entry points

• references/training-workflows.md
• references/model-overview.md
• references/troubleshooting.md
• scripts/make_training_config.py

Use for

• choosing a training stage before a run
• generating or editing a training config
• understanding *_gen.pth, *_dis.pth, samples/, results/, and log_*.dat
• explaining why a run resumed, stalled, or produced unexpected losses

Do not use for

• dataset flist construction or path validation
• test-time command wiring
• PSNR/SSIM/FID scoring after inference