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name: training-and-finetuning description: "Route GeoAI training, fine-tuning, evaluation, and safe Hub publishing." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training and Finetuning

Use this sub-skill when the task is to prepare training data layouts, select a GeoAI training API, fit or fine-tune a model, inspect losses or metrics, or publish a trained checkpoint to Hugging Face Hub.

If the request is inference-only, route it to the detection-segmentation-inference path instead. If the work still needs image download, raster/vector tiling, or format conversion before training, hand that preparation to the geospatial-data-pipelines path first. If the task is about foundation-model processors, embeddings, or VLMs, route it to the foundation-models-embeddings-vlms path.

What this sub-skill covers

• geoai.segmentation for SegFormer-style paired image/mask training.
• geoai.train for semantic segmentation, instance segmentation, object detection, validation, and training-path checks.
• geoai.timm_train, geoai.timm_segment, and geoai.timm_regress for timm-based classification, segmentation, and pixel regression.
• geoai.classify and geoai.recognize for geospatial classification workflows.
• geoai.landcover_train and geoai.landcover_utils for landcover losses, sparse-label metrics, class weights, and tile export.
• geoai.object_detect for multi-class detector preparation, evaluation, and publish-time helpers.

Start here

1. Read references/training-recipes.md for the safest recipe match.
2. Read references/api-reference.md for verified callable names, signatures, and return shapes.
3. Read references/troubleshooting.md when the layout, channels, labels, checkpoint, or dependency story is unclear.
4. Run scripts/check_training_layout.py to validate a candidate training layout before choosing a trainer.

Guardrails

• Do not start training, data downloads, or Hub pushes from this router text.
• Keep push_*_to_hub calls credential-aware and explicit; they create or update remote repositories.
• Keep layout validation local and read-only.
• Prefer the smallest trainer that matches the data layout and label semantics.