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name: model-and-config description: "Route GeoSeg model-family, dataset-label, loss, optimizer, checkpoint, and configuration-inspection questions to verified repository guidance." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Model and configuration

Use this sub-skill when the question is about selecting a GeoSeg model, matching an output head to a dataset, choosing losses or auxiliary supervision, understanding optimizer/checkpoint fields, or inspecting a Python config without accidentally requiring the dataset. Start with the relevant catalog rather than importing a config blindly:

• model-overview.md — model families, output behavior, dataset classes, palettes, and ignore-index conventions.
• configuration.md — the nine checked-in dataset/model configurations and their training-oriented settings.
• api-reference.md — constructor, loss, config, optimizer, and metric API details.
• troubleshooting.md — dependency, data, config, CLI, backend, and workflow failure diagnosis.
• inspect_config.py — a static, non-importing config report helper.

For actual training, hand off to training. For model checkpoint execution or tiled/large-image prediction, hand off to evaluation-inference. This sub-skill does not prescribe a full training run or checkpoint-inference procedure.

Safe decision sequence

1. Identify the dataset and read its class order, palette, and ignore index.
2. Select a model whose final head has exactly num_classes channels; enable auxiliary loss only when the model returns the expected training outputs.
3. Confirm whether pretrained means a timm download/cache or an explicit local checkpoint load, and check every referenced weight path.
4. Inspect a config statically first with scripts/inspect_config.py. Only execute py2cfg after data directories, optional dependencies, and weight files are intentionally available; several configs perform work at import.
5. Validate a synthetic batch's logits and label range before delegating the run to the training or evaluation-inference sibling.

The source snapshot used for this graph is commit 9453fe48209c4626b29e35e61bab93b61212c4b1. The inspection record confirms Python 3.8 documentation, the listed PyTorch/CUDA stack, and an A100 CUDA smoke check. No dataset, checkpoint, native test, or example is present in that checkout; treat unverified optional families and external assets as explicit prerequisites, not as available capabilities.