| name | tao-train-dino |
| description | DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with denoising training, multi-scale features, and optional distillation support. Use when training, evaluating, exporting, distilling, quantizing, or running inference for a TAO DINO detector. Trigger phrases include "train DINO", "DETR object detection", "TAO 2D detection", "DINO with distillation". |
| 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 |
DINO
DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with denoising training, multi-scale features, and optional distillation support.
Uses pretrained backbone weights (e.g. ResNet-50 ImageNet). Set model.pretrained_backbone_path for backbone-only or train.pretrained_model_path for full model.
When To Use
Train, evaluate, export, distill, quantize, or run inference for a TAO DINO 2D object detector.
For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and
TensorRT inference), read references/tao-deploy-dino.md first. Deploy spec templates live
in this skill's references/ folder with the spec_template_deploy_*.yaml
prefix.
Reference Map
references/dino-data-specs.md — dataset contracts, per-action dataset requirements, per-action spec-override examples (train, evaluate, export, deploy/gen_trt_engine, inference, quantize, distill), data-source arrays, checkpoint inference, and dataset layout.references/dino-actions-errors.md — important parameters, default values, evaluate/export defaults, hardware, and the full error-pattern catalog.references/dino-tuning-multigpu.md — full AutoML/HPO notes (metrics, hyperparameters, extractor) and multi-GPU spec consistency.references/dino-automl-sdk.md — AutoML metrics, SDK orchestration internals, data-source gap, and spec-param/parent-model inference.references/tao-deploy-dino.md — TensorRT deploy workflow.references/detailed-guide.md — map to the detailed model guide.
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. 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.
Training Requirements
The agent MUST read this section before generating any training or AutoML script for DINO.
- Dataset type: object_detection
- Formats: coco, coco_raw
- Accepted dataset intents: training, evaluation, testing, calibration
- Monitoring metric: mAP50 for quick operational checks;
val_mAP for
COCO/paper-style benchmark comparisons.
Required datasets — MUST resolve both:
| Dataset | Required | Why |
|---|
| Train dataset URI | Yes | Training data (COCO format) |
| Validation dataset URI | Yes — ALWAYS | DINO unconditionally builds a val dataloader. Omitting val_data_sources causes FileNotFoundError at startup regardless of the metric or workflow. If the user has no separate eval split, reuse the train URI. |
Required inputs before generating any training spec:
- Train dataset URI — S3 path to COCO-format training data
- Validation dataset URI — S3 path to COCO-format val data (can be same as train)
num_classes — How many object classes? Default 91 (COCO). Must be >= max(category_id) + 1. Too low causes CUDA error: device-side assert triggered.
Resolve these from the user request or the default profile below. Prompt only
for values that are still missing after applying the profile rules.
Bankable local default profile for DINO AutoML smoke runs:
Use this profile only when the user asks to run DINO AutoML and does not provide
dataset or class-count inputs. This profile is intentionally small and local to
this skill bank; it is for smoke/iteration runs, not a production benchmark.
Do not search previous runners, logs, session state, shell history, or the home
directory to recover these values.
DINO_AUTOML_PROFILE = {
"train_dataset_uri": "s3://nvcf-storage-handling/data/tao_od_synthetic_subset_train_no_convert",
"validation_dataset_uri": "s3://nvcf-storage-handling/data/tao_od_synthetic_subset_val_no_convert",
"object_classes": 4,
"dataset_num_classes": 5,
"image_archive": "images.tar.gz",
"annotation_file": "annotations.json",
"max_recommendations": 10,
"train_num_epochs": 10,
"train_checkpoint_interval": 10,
"train_validation_interval": 1,
"train_num_gpus": 1,
}
If the user supplies any dataset URI or class-count value, prefer the user value
and ask for any remaining required DINO value. Do not partially mix a user's
custom dataset with this profile's class count unless the user confirms it.
Do not prompt for image layout for the standard DINO dataset. The standard
TAO DINO dataset artifact is images.tar.gz plus annotations.json. Use
images.tar.gz in the remote image_dir spec override. The SDK downloads the
archive and rewrites the runtime spec to the extracted folder named after the
archive stem (images.tar.gz -> images). Only deviate if the user explicitly
provides a different image artifact name.
Core Workflow
DINO supports train, evaluate, export, distill, quantize, and inference. Data-source
overrides are mandatory for every action — DINO's config.json has empty
data_sources because the runner cannot auto-resolve array-of-objects spec keys.
The agent MUST construct data source paths and include them in spec_overrides.
See references/dino-data-specs.md for the per-action dataset requirements table,
the standard dataset artifact (images.tar.gz + annotations.json) and runtime
folder rewrite rules, and the complete per-action spec_overrides examples for
train, evaluate, export, deploy/gen_trt_engine, inference, quantize, and distill —
including checkpoint inference via parent_model, the results_dir/train/
checkpoint location, and the distillation FAN-teacher / student rules.
Important Parameters And Defaults
Key defaults: num_epochs=10, batch_size=4, learning_rate=2e-4,
lr_backbone=2e-5, num_classes=91, backbone=resnet_50.
- dataset.num_classes: Default 91 (COCO). Must be >=
max(category_id) + 1. Too low causes CUDA error: device-side assert triggered. Set as <num_classes> + 1 in spec overrides.
- num_epochs: default 10 (quick iteration); real datasets typically need 30-50+ epochs for good mAP.
See references/dino-actions-errors.md for the full parameter list (backbone
options, train.optim.lr/lr_steps, model.num_queries, batch_size),
default values, evaluate defaults, export defaults (input 960x544, opset 17,
TRT data types, workspace 1024 MB), and hardware requirements.
Multi-GPU And AutoML / HPO
When increasing train.num_gpus, also set train.gpu_ids to the same visible
device range, or distributed startup can be inconsistent.
AutoML runs training — all Training Requirements above apply. For no-input
local smoke runs, use DINO_AUTOML_PROFILE. Recommended metric is mAP50
(val_mAP for benchmark comparisons) with direction="maximize" and a custom
metric_extractor.
See references/dino-tuning-multigpu.md for the full multi-GPU spec-consistency
rule (8-GPU example, NCCL timeout note) and the full AutoML/HPO notes (metric
selection, metric_extractor, recommended hyperparameters, weight_decay
behavior, dense-dataset resume guidance). See references/dino-automl-sdk.md for
AutoML metric extractor code, SDK orchestration internals, and parent-model
inference mappings.
Error Patterns
Common failures include CUDA OOM (reduce batch_size), missing val_data_sources
(FileNotFoundError at startup — always supply val), num_classes too low (CUDA device-side assert), and the parent dino gen_trt_engine / dino convert PyT-CLI
restrictions.
See references/dino-actions-errors.md for the complete error-pattern catalog
with diagnostics and fixes.
Spec Param / Parent Model Inference
Model-specific inference mappings belong in this MD file, not in config.json.
Generated runners read the mappings and apply them with SDK helpers before
create_job(). 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.
See references/dino-automl-sdk.md for the full inference-mapping table (per
action: parent_model, key, output_dir, ptm_if_no_resume_model,
resume_model, create_onnx_file) and the TensorRT-mapping note. TensorRT
mappings live in the deploy workflow, not the PyT model skill.
Optional: running via the TAO SDK
When running DINO through the TAO SDK (script_runner orchestration, S3 I/O
wrapping, AutoML), skills read references/skill_info.yaml for input and
spec-param mappings. See references/dino-automl-sdk.md for SDK orchestration
internals, including the data-sources gap and the [0]-indexed inputs
declarations. Skip this when running locally with docker run.
Deployment
