tao-train-rtdetr

name	tao-train-rtdetr
description	RT-DETR (Real-Time DEtection TRansformer) for 2D object detection. Designed for real-time inference with competitive accuracy and supports distillation and quantization for deployment optimization. Use when training, evaluating, distilling, quantizing, exporting, or running inference for a TAO RT-DETR model. Trigger phrases include "train RT-DETR", "real-time DETR", "low-latency object detection", "RT-DETR distillation / quantization".
license	Apache-2.0
compatibility	Requires docker + nvidia-container-toolkit.
metadata	{"version":"0.1.0","author":"NVIDIA Corporation"}
allowed-tools	Read Bash
tags	["object","detection"]

RT-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).

RT-DETR (Real-Time DEtection TRansformer) for 2D object detection. Designed for real-time inference with competitive accuracy. Supports distillation and quantization for deployment optimization.

Set model.pretrained_backbone_path for backbone weights or train.pretrained_model_path for full model.

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

Dataclass Schemas

Generated TAO Core schemas are packaged in schemas/<action>.schema.json, with schemas/manifest.json listing available actions. Each generated schema also emits references/spec_template_<action>.yaml from the schema top-level default field. AutoML enablement is declared at the model layer in references/skill_info.yaml via automl_enabled. Runnable AutoML for an action requires schemas/<action>.schema.json and references/spec_template_<action>.yaml to exist and parse. Use the packaged selected-action schema for automl_default_parameters, automl_disabled_parameters, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ~/tao-core at runtime; maintainers regenerate schemas/templates before packaging the skill bank.

Train Action Policy

This model is AutoML-enabled at the model layer. Before handling any train-stage request, read references/skill_info.yaml and resolve the run override from either an explicit automl_policy value or the user's workflow request. Use automl_policy: on by default and only expose on / off in new launch prompts. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as automl_policy: off for this run only. When automl_policy: on, automl_enabled: true, and both schemas/train.schema.json and references/spec_template_train.yaml are packaged, route the train action through tao-skill-bank:tao-run-automl by default with this model's skill_dir. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and automl_policy. Use direct model training only when automl_policy: off or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.

Non-train actions such as evaluate, inference, export, and deploy flows stay in this model skill. The per-run automl_policy override does not change model metadata.

Supported Actions

The packaged RT-DETR PyT CLI supports train, distill, quantize, evaluate, export, inference, and default_specs. This model skill exposes train, distill, quantize, evaluate, export, and inference; resume/retrain is performed through train with train.resume_training_checkpoint_path.

The parent PyT CLI does not expose gen_trt_engine. Use models/rtdetr/deploy for TensorRT engine generation, TensorRT evaluation, and TensorRT inference.

Training Requirements

• Dataset type: object_detection
• Formats: coco, coco_raw
• AutoML training metrics: val_mAP50 for quick operational checks or val_mAP for COCO/paper-style benchmark comparisons; both are maximized. These are the exact structured train-status KPI names.
• Standalone evaluation metrics: test_mAP50 and test_mAP. Do not use the evaluator's test_* names for AutoML trial ranking.

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
distill	dataset.train_data_sources	train_datasets	image_dir: images.tar.gz, json_file: annotations.json	Yes
distill	dataset.val_data_sources	eval_dataset	image_dir: images.tar.gz, json_file: annotations.json	No
evaluate	dataset.test_data_sources	eval_dataset	image_dir: images.tar.gz, json_file: annotations.json	No
inference	dataset.infer_data_sources	inference_dataset	image_dir: images.tar.gz, classmap: label_map.txt	Yes
quantize	dataset.train_data_sources	train_datasets	image_dir: images.tar.gz, json_file: annotations.json	Yes
quantize	dataset.val_data_sources	eval_dataset	image_dir: images.tar.gz, json_file: annotations.json	No
quantize	dataset.quant_calibration_data_sources	train_datasets	image_dir: images.tar.gz, json_file: annotations.json	No
train	dataset.train_data_sources	train_datasets	image_dir: images.tar.gz, json_file: annotations.json	Yes
train	dataset.val_data_sources	eval_dataset	image_dir: images.tar.gz, json_file: annotations.json	No

Typical Spec Overrides

Data source overrides are mandatory for every action — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in spec_overrides.

S3_TRAIN = "s3://bucket/data/train"
S3_EVAL = "s3://bucket/data/eval"
CHECKPOINT = "/results/{train_job_id}/results_dir/model_epoch_000.pth"
ONNX_FILE = "/results/{export_job_id}/results_dir/rtdetr.onnx"

train (mandatory data sources):

{
    "train.num_epochs": 10,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "train.gpu_ids": [0],
    "dataset.num_classes": "<num_classes> + 1",
    "dataset.train_data_sources": [{"image_dir": f"{S3_TRAIN}/images.tar.gz", "json_file": f"{S3_TRAIN}/annotations.json"}],
    "dataset.val_data_sources": {"image_dir": f"{S3_EVAL}/images.tar.gz", "json_file": f"{S3_EVAL}/annotations.json"},
}

resume train (mandatory checkpoint):

{
    "train.num_epochs": 11,
    "train.resume_training_checkpoint_path": CHECKPOINT,
    "dataset.num_classes": "<num_classes> + 1",
    "dataset.eval_class_ids": [1, 2, 3, 4],
    "dataset.train_data_sources": [{"image_dir": f"{S3_TRAIN}/images.tar.gz", "json_file": f"{S3_TRAIN}/annotations.json"}],
    "dataset.val_data_sources": {"image_dir": f"{S3_EVAL}/images.tar.gz", "json_file": f"{S3_EVAL}/annotations.json"},
}

evaluate (mandatory data sources and checkpoint):

{
    "dataset.num_classes": "<num_classes> + 1",
    "dataset.eval_class_ids": [1, 2, 3, 4],
    "dataset.test_data_sources": {"image_dir": f"{S3_EVAL}/images.tar.gz", "json_file": f"{S3_EVAL}/annotations.json"},
    "evaluate.checkpoint": CHECKPOINT,
}

export (mandatory checkpoint and output):

{
    "dataset.num_classes": "<num_classes> + 1",
    "export.checkpoint": CHECKPOINT,
    "export.onnx_file": ONNX_FILE,
    "export.input_height": 640,
    "export.input_width": 640,
}

quantize (mandatory data sources):

{
    "dataset.num_classes": "<num_classes> + 1",
    "quantize.layers": [
        {
            "module_name": "*",
            "weights": {
                "dtype": "float8_e4m3fn"
            },
            "activations": {
                "dtype": "float8_e4m3fn"
            }
        }
    ],
    "dataset.train_data_sources": [{"image_dir": f"{S3_TRAIN}/images.tar.gz", "json_file": f"{S3_TRAIN}/annotations.json"}],
    "dataset.val_data_sources": {"image_dir": f"{S3_EVAL}/images.tar.gz", "json_file": f"{S3_EVAL}/annotations.json"},
    "dataset.quant_calibration_data_sources": {"image_dir": f"{S3_TRAIN}/images.tar.gz", "json_file": f"{S3_TRAIN}/annotations.json"},
    "quantize.model_path": CHECKPOINT,
}

inference (mandatory data sources and checkpoint):

{
    "dataset.num_classes": "<num_classes> + 1",
    "dataset.infer_data_sources": {"image_dir": [f"{S3_EVAL}/images.tar.gz"], "classmap": f"{S3_EVAL}/label_map.txt"},
    "inference.checkpoint": CHECKPOINT,
}

distill (mandatory data sources and teacher checkpoint):

{
    "dataset.train_data_sources": [{"image_dir": f"{S3_TRAIN}/images.tar.gz", "json_file": f"{S3_TRAIN}/annotations.json"}],
    "dataset.val_data_sources": {"image_dir": f"{S3_EVAL}/images.tar.gz", "json_file": f"{S3_EVAL}/annotations.json"},
    "distill.pretrained_teacher_model_path": CHECKPOINT,
}

Eval Dataset

Optional. Provides validation mAP at each checkpoint if supplied.

Important Parameters

• dataset.num_classes: Number of classes. Default 80 (MSCOCO 80-class). Must match your dataset annotations.
• model.backbone: Default resnet_50. Supported: ResNet variants, ConvNeXt, FAN, EfficientViT. RT-DETR is optimized for real-time with lighter backbones.
• train.optim.lr: Learning rate. Default 1e-4 (lower than DINO's 2e-4). lr_backbone defaults to 1e-5.
• dataset.augmentation.train_spatial_size: Training input size. Default [640, 640]. Smaller than DINO's multi-scale (up to 1333). Key to RT-DETR's speed.
• model.num_feature_levels: Default 3 (vs DINO's 4). return_interm_indices is [1,2,3].
• train.enable_ema: Exponential moving average. Default False. Enable for potentially smoother convergence.
• dataset.remap_mscoco_category: Default False. Set True only for original MSCOCO dataset with 91-to-80 category ID remapping.

Multi-GPU / Multi-Node

Launch method: torchrun (LIGHTNING_EXCLUDED_NETWORK). The entrypoint runs torchrun --nnodes=N --nproc-per-node=M train.py, NOT plain python.

Spec Key	Description	Default
train.num_gpus	Number of GPUs per node	1
train.gpu_ids	GPU device indices	[0]
train.num_nodes	Number of nodes	1
train.distributed_strategy	ddp or fsdp	ddp

• When increasing train.num_gpus, also set train.gpu_ids to the same visible device range. For example, an 8-GPU single-node Slurm run must include both "train.num_gpus": 8 and "train.gpu_ids": [0, 1, 2, 3, 4, 5, 6, 7].
• CUDA_VISIBLE_DEVICES is explicitly set (unlike Lightning-managed models which use TAO_VISIBLE_DEVICES)
• ddp with activation checkpointing: find_unused_parameters=False
• ddp without: find_unused_parameters=True
• fsdp supported, forces FP16

Multi-node env vars (set by orchestrator):

Variable	Purpose
WORLD_SIZE	Number of nodes (triggers multinode mode)
NODE_RANK	This node's rank (0-indexed)
MASTER_ADDR	Rank-0 node IP
MASTER_PORT	Rank-0 port (default 29500)
NUM_GPU_PER_NODE	GPUs per node (default: all visible)

CRITICAL: NODE_RANK is copied to RANK if RANK is unset. This is required for torchrun multinode.

Export / TRT Defaults

• Export input: 640x640, opset 17
• TRT data types: FP32, FP16, INT8
• TRT workspace: 1024 MB
• TRT max_batch_size: 4

Distillation

RT-DETR supports knowledge distillation with a teacher model. Requires distill action with distill.pretrained_teacher_model_path and a distillation binding configuration.

Use the packaged references/spec_template_distill.yaml as the starting point. The validated default binding uses the RT-DETR distiller's explicit IOU feature path:

distill:
  bindings:
  - student_module_name: srcs
    teacher_module_name: srcs
    criterion: IOU
    weight: 1.0

Do not substitute DINO-style output names such as pred_logits / pred_boxes, and do not bind arbitrary decoder heads unless you have verified the module returns captured feature lists. The RT-DETR distiller asserts that IOU bindings must use srcs or dsrcs.

Hardware

Minimum 1 GPU(s), recommended 2 GPU(s). 16GB+ (V100 or A100) VRAM per GPU. RT-DETR is more memory-efficient than DINO/GDINO due to smaller input size (640x640) and fewer feature levels. Trains well on single GPU for small-medium datasets.

Error Patterns

CUDA out of memory: Reduce batch_size. RT-DETR at 640x640 is lighter than DINO at 1333px, but batch_size > 8 may still OOM on 16GB GPUs.

num_classes mismatch: RT-DETR defaults to 80 (not 91 like DINO). Ensure dataset.num_classes matches your annotation categories.

CUDA index assert from category IDs: If COCO category IDs are one-based or otherwise not remapped to zero-based contiguous IDs, set dataset.num_classes to max(category_id) + 1 and keep dataset.eval_class_ids aligned to the actual category IDs. For the packaged four-class S3 sample with IDs 1-4, use dataset.num_classes: 5 and dataset.eval_class_ids: [1, 2, 3, 4].

return_interm_indices vs num_feature_levels: Default is [1,2,3] with num_feature_levels=3. Must be consistent if changed.

Export shape mismatch: Keep RT-DETR export and deploy consumer input size at the validated 640x640 default unless the model has been trained and checked for a different shape. The older packaged 960x544 template shape can fail during ONNX tracing with The size of tensor a (...) must match the size of tensor b (...) in hybrid_encoder.py positional embedding addition.

AutoML metric extraction: RT-DETR emits detection metrics in structured training status and logs. For COCO/paper-style benchmark comparisons, optimize val_mAP with direction: maximize; for explicit AP50 workflows, optimize val_mAP50. Standalone evaluation emits the corresponding test_mAP and test_mAP50 keys. Prefer results_dir/train/status.json or AutoML result state before parsing raw logs. Do not optimize val_loss for default detection model invocations.

Checkpoint handoff: For evaluate/export/inference/quantize/distill/resume, use the checkpoint resolver on the best AutoML child job's results_dir/train/ folder and select the action-appropriate model_epoch_*.pth checkpoint. RT-DETR may also write a latest symlink, but that should only be used when a caller explicitly requests latest. Keep dataset.num_classes, dataset.eval_class_ids, model.num_queries, and model.num_select consistent with training.

Parent rtdetr gen_trt_engine rejected by the PyT CLI: In the validated 7.0.0 PyT container, rtdetr gen_trt_engine is not a valid parent-model subtask. Use the RT-DETR deploy workflow (references/tao-deploy-rtdetr.md) for TensorRT engine generation, TensorRT evaluation, and TensorRT inference.

Spec Param / Parent Model Inference

Model-specific inference mappings belong in this MD file, not in config.json. Generated runners should read this section and apply the mappings with SDK helpers before create_job(). This mirrors the old microservices infer_params.py flow.

Inference mappings from TAO Core rtdetr.config.json:

Action	Spec Field	Inference Function	Meaning
distill	distill.pretrained_teacher_model_path	parent_model	model file inferred from the parent job results folder
distill	encryption_key	key	encryption key
distill	results_dir	output_dir	current job results directory
evaluate	encryption_key	key	encryption key
evaluate	evaluate.checkpoint	parent_model	model file inferred from the parent job results folder
evaluate	evaluate.trt_engine	parent_model	model file inferred from the parent job results folder
evaluate	results_dir	output_dir	current job results directory
export	encryption_key	key	encryption key
export	export.checkpoint	parent_model	model file inferred from the parent job results folder
export	export.onnx_file	create_onnx_file	output ONNX path
export	results_dir	output_dir	current job results directory
inference	encryption_key	key	encryption key
inference	inference.checkpoint	parent_model	model file inferred from the parent job results folder
inference	inference.trt_engine

For parent_model or parent_model_folder, pass the upstream train/export/AutoML child job id as parent_job_id. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to config.json and do not patch generated runner scripts to guess checkpoint paths.

Deployment

• tao-deploy-rtdetr