RT-DETR

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 still requires

schemas/train.schema.json

and

references/spec_template_train.yaml

to exist and parse. Use the packaged train 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. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as

automl_policy: off

for this run only; otherwise default to

auto

. When

automl_policy: auto

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.

Training Requirements

Dataset type: object_detection
Formats: coco, coco_raw
Monitoring metric: val_mAP50

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
gen_trt_engine	gen_trt_engine.tensorrt.calibration.cal_image_dir	calibration_dataset	images.tar.gz	Yes
inference	dataset.infer_data_sources	inference_dataset	image_dir: images.tar.gz, classmap: label_map.txt	No
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

python

S3_TRAIN = "s3://bucket/data/train"
S3_EVAL = "s3://bucket/data/eval"

train (mandatory data sources):

python

{
    "train.num_epochs": 10,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "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"},
}

evaluate (mandatory data sources):

python

{
    "dataset.num_classes": "<num_classes> + 1",
    "dataset.test_data_sources": {"image_dir": f"{S3_EVAL}/images.tar.gz", "json_file": f"{S3_EVAL}/annotations.json"},
}

export:

python

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

quantize (mandatory data sources):

python

{
    "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"},
}

gen_trt_engine (mandatory data sources):

python

{
    "gen_trt_engine.tensorrt.data_type": "FP16",
    "gen_trt_engine.tensorrt.calibration.cal_image_dir": [f"{S3_TRAIN}/images.tar.gz"],
}

inference (mandatory data sources):

python

{
    "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"},
}

distill (mandatory data sources):

python

{
    "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"},
}

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`

```
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

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

Full TAO Deploy reference: tao-deploy-rtdetr.

Distillation

RT-DETR supports knowledge distillation with a teacher model. Requires

distill

action with teacher model path and distillation bindings configuration.

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.

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

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
gen_trt_engine	`encryption_key`	`key`	encryption key
gen_trt_engine	`gen_trt_engine.onnx_file`	`parent_model`	model file inferred from the parent job results folder
gen_trt_engine	`gen_trt_engine.tensorrt.calibration.cal_cache_file`	`create_cal_cache`	calibration cache path
gen_trt_engine	`gen_trt_engine.trt_engine`	`create_engine_file`	output TensorRT engine path
gen_trt_engine	`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`	`parent_model`	model file inferred from the parent job results folder
inference	`results_dir`	`output_dir`	current job results directory
quantize	`encryption_key`	`key`	encryption key
quantize	`quantize.model_path`	`parent_model`	model file inferred from the parent job results folder
quantize	`results_dir`	`output_dir`	current job results directory
train	`encryption_key`	`key`	encryption key
train	`model.pretrained_backbone_path`	`ptm_if_no_resume_model`	PTM when no resume checkpoint exists
train	`results_dir`	`output_dir`	current job results directory
train	`train.pretrained_model_path`	`ptm_if_no_resume_model`	PTM when no resume checkpoint exists
train	`train.resume_training_checkpoint_path`	`resume_model`	model file inferred from the current job results folder

For

parent_model

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.

tao-train-rtdetr

NPX Install

Tags

SKILL.md Content

RT-DETR

Dataclass Schemas

Train Action Policy

Training Requirements

Per-Action Dataset Requirements

Typical Spec Overrides

Eval Dataset

Important Parameters

Multi-GPU / Multi-Node

Export / TRT Defaults

Distillation

Hardware

Error Patterns

Spec Param / Parent Model Inference