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tao-train-ocdnet

OCDNet for scene text detection. Detects arbitrary-oriented text regions in natural images using a differentiable binarization approach. Use when training, evaluating, exporting, pruning, quantizing, retraining, or running inference for a TAO OCDNet model. Trigger phrases include "train OCDNet", "scene text detection", "arbitrary-oriented text boxes", "differentiable binarization detector".

skills/tao-train-ocdnet/SKILL.md

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# OCDNet

> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first (host preflight, credentials, cross-skill discovery).

OCDNet for scene text detection. Detects arbitrary-oriented text regions in natural images using a differentiable binarization approach.

Set `model.pretrained_model_path` for pretrained weights.

For TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), read `references/tao-deploy-ocdnet.md` first. Deploy spec templates live in this skill's `references/` folder with the `spec_template_deploy_*.yaml` prefix.

The PyT OCDNet CLI supports `train`, `evaluate`, `export`, `inference`, `prune`, `quantize`, and `default_specs`. It does not expose PyT-side `retrain` or `gen_trt_engine` subcommands. The model skill exposes `retrain` by running `ocdnet train` with `model.load_pruned_graph: true` and `model.pruned_graph_path`. Resume from an epoch checkpoint uses `ocdnet train` plus `train.resume_training_checkpoint_path`. TensorRT engine generation is owned by the deploy workflow.

## Dataclass Schemas