tao-train-codetr

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:

# 1. console script (preferred when present)
docker run --rm "$TAO_PYT_IMAGE" codetr --help >/dev/null 2>&1 && CODETR="codetr"

# 2. module fallback — works whenever the package is installed
[ -z "${CODETR:-}" ] && docker run --rm --entrypoint sh "$TAO_PYT_IMAGE" \
  -c 'python3 -c "import nvidia_tao_pytorch.cv.codetr"' >/dev/null 2>&1 \
  && CODETR="python3 -m nvidia_tao_pytorch.cv.codetr.entrypoint.codetr"

[ -z "${CODETR:-}" ] && { echo "FATAL: Co-DETR not available in $TAO_PYT_IMAGE"; exit 1; }

Both forms take identical arguments — the module entrypoint reports itself as codetr and exposes the same {train, evaluate, inference, default_specs} subtasks. Substitute $CODETR wherever this document writes codetr.

Only if both probes fail does this image genuinely lack Co-DETR. Then stop and ask which image to use — do not silently substitute another detector.

Dataclass Schemas

Generated TAO Core schemas are not yet packaged for this model, so schemas/<action>.schema.json and references/spec_template_<action>.yaml are absent except for the hand-written references/spec_template_inference.yaml. Read spec keys from the upstream experiment specs in nvidia_tao_pytorch/cv/codetr/experiment_specs/ (train.yaml, eval.yaml, inference.yaml, export.yaml) until a maintainer regenerates them. AutoML is therefore not runnable for this model.