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name: models-and-customization description: "Use docTR standalone model factories, custom weights, vocabulary whitelists, Hugging Face Hub models, ONNX export, and PyTorch device/precision/compile optimization safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

models-and-customization

Use this sub-skill when the task is about docTR model selection or model-level customization rather than end-to-end document I/O:

• Choosing detection, recognition, layout, table, classification, crop-orientation, or page-orientation architectures.
• Instantiating standalone predictors such as detection_predictor, recognition_predictor, layout_predictor, table_predictor, crop_orientation_predictor, and page_orientation_predictor.
• Loading custom weights or custom trained models into docTR predictors.
• Restricting recognition outputs with vocabulary whitelists.
• Loading or sharing models through the Hugging Face Hub interface.
• Exporting PyTorch models to ONNX or optimizing inference with devices, batch sizes, half precision, or torch.compile.

Do not use this sub-skill as the primary guide for full OCR/KIE pipelines, document loading/export objects, dataset formats, training commands, CLI usage, or service deployment. Route those to the corresponding sibling sub-skill, then return here only for the model-level details.

Operating procedure

1. Start with references/model-catalog-and-customization.md to pick the correct architecture, factory signature, custom loading pattern, Hub entry point, or whitelist strategy.
2. Use references/optimization-and-export.md before changing devices, precision, compile mode, batch sizes, or ONNX export settings.
3. If anything fails, read references/troubleshooting.md before changing architectures or assuming a package bug.
4. Keep user code explicit about pretrained: pretrained=True can trigger model downloads; pretrained=False creates randomly initialized models and is not useful for real inference unless weights are loaded afterward.

Quick routing cues

• "Which architecture should I use?" → model catalog.
• "I trained weights; how do I plug them into docTR?" → custom loading section in the model catalog.
• "Only allow German/Latin characters" → whitelist section in the model catalog.
• "Use a model from Hugging Face" → Hub section in the model catalog.
• "CUDA, MPS, BF16, FP16, compile, ONNX" → optimization/export reference.
• "Unknown architecture, state dict mismatch, CPU half precision, ONNX shape/names" → troubleshooting reference.