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tao-train-codetr

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Co-DETR (CoDINO) for object detection. A DETR-family detector with collaborative hybrid assignment — auxiliary one-to-many heads supervise the encoder during training, giving strong closed-set accuracy at high inference cost. Use when training, evaluating, or running inference for a TAO Co-DETR model. Trigger phrases include "train Co-DETR", "run CoDINO", "codetr inference", "collaborative DETR", "autolabel detections with Co-DETR".

skills/tao-train-codetr/SKILL.md

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Nov 9, 2026

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Repository last updated
Nov 11, 2026
License
Apache-2.0

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# Co-DETR

> **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).

Co-DETR trains a DETR detector alongside auxiliary one-to-many assignment heads (`num_co_heads`), which supervise the encoder more densely than DETR's one-to-one matching alone. The auxiliary heads exist only during training; inference runs the primary DETR head. Backbones are large by default — `vit_large_codetr` for training, `swin_large_patch4_window7_224` for the reference inference/eval configs — so this is an accuracy-first model, not a latency-first one.

## Availability — probe, then fall back to the module

The `codetr` **console script is not registered** in the TAO PyTorch images checked so far
(`7.0.1-pyt` and the `2026.7.31-rc-12-multiarch` nightly), even though the module itself
ships. So a bare `codetr` invocation fails while the network is perfectly usable.

Probe in two steps and use whichever works:
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