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.
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.
Generated TAO Core schemas are packaged in schemas/<action>.schema.json (with schemas/manifest.json listing actions); each schema emits a matching references/spec_template_<action>.yaml. See references/sdk_orchestration.md for the full dataclass-schema, spec-template, data-sources, and parent-model inference details used by SDK orchestration.
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
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: val_mAP50
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 causesCUDA 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.
Spec Overrides, Datasets, and Parameters
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 build data source paths and include them in spec_overrides.
See references/spec_overrides.md for: the per-action dataset requirements table; the mandatory spec_overrides blocks for train, evaluate, export, gen_trt_engine, inference, quantize, and distill; checkpoint resolution via parent_model inference and the results_dir/train/dino_model_latest.pth fallback; the COCO dataset format and images.tar.gz archive-stem rules; per-action data-source layouts; the full Important Parameters list (num_classes, backbone and its supported values, lr/lr_backbone, num_epochs, lr_steps, num_queries, batch_size); Default Values (num_epochs 10, batch_size 4, learning_rate 2e-4, lr_backbone 2e-5, num_classes 91, backbone resnet_50); Evaluate Defaults; Export Defaults (input 640x640, opset 17, trt_data_types [FP32, FP16, INT8], trt_workspace_size_mb 1024); and Hardware guidance (1 GPU minimum, 4 recommended, 24GB+ A100). Full TAO Deploy reference: tao-deploy-dino.
When generating an evaluate spec, carry forward the winning AutoML rec's structural model settings (model.backbone, model.num_queries, model.dropout_ratio, dataset.num_classes) so the checkpoint shape matches the evaluation model.
Error Patterns
Common failures include CUDA OOM, num_select < num_queries * num_classes, spec/schema merge errors, dataset-smaller-than-batch, return_interm_indices vs num_feature_levels mismatch, FileNotFoundError on images or missing val data, CUDA device-side assert from low num_classes, S3 inputs not downloaded, and evaluate checkpoint not found at the result root. See references/troubleshooting.md for each error pattern and its fix.
AutoML / HPO Notes
AutoML runs training — all Training Requirements above apply, and the no-input case uses DINO_AUTOML_PROFILE. Do not inspect previous AutoML runs to infer dataset URIs, num_classes, recommendation count, or interval settings. Use explicit metric="mAP50" with direction="maximize" and a custom metric_extractor reading Validation mAP50 rather than metric="kpi". See references/automl.md for the recommended metric extractor, hyperparameter list, custom_param_ranges, the train.optim.weight_decay note, and the backbone-constraint guidance.
Optional: running via the TAO SDK
The SDK script_runner orchestration, S3 I/O wrapping, AutoML internals, spec-template generation, the data-sources gap, the config.json inputs declarations, and the full per-action spec-param / parent-model inference mapping table are documented in references/sdk_orchestration.md. Skip this when running locally with docker run.