Mask Grounding DINO for grounded instance segmentation. Extends Grounding DINO with a mask-prediction head for open-set segmentation guided by text prompts. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Mask-Grounding-DINO model. Trigger phrases include "train Mask Grounding DINO", "open-vocabulary segmentation", "text-prompted instance segmentation", "grounded mask DETR".
Mask Grounding DINO for grounded instance segmentation. Extends Grounding DINO with a mask-prediction head for open-set segmentation guided by text prompts. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Mask-Grounding-DINO model. Trigger phrases include "train Mask Grounding DINO", "open-vocabulary segmentation", "text-prompted instance segmentation", "grounded mask DETR".
license
Apache-2.0
compatibility
Requires docker + nvidia-container-toolkit.
metadata
{"version":"0.1.0","author":"NVIDIA Corporation"}
allowed-tools
Read Bash
tags
["segmentation"]
Mask Grounding DINO
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the skill first (host preflight, credentials, cross-skill discovery).
tao-setup
Mask Grounding DINO for grounded instance segmentation. Extends Grounding DINO with mask prediction head for open-set segmentation guided by text prompts.
Set train.pretrained_model_path for full model weights.
For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-mask-grounding-dino.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.
Training Requirements
Dataset type: segmentation
Formats: odvg, coco, coco_raw
Monitoring metric: val_loss
AutoML metric contract: Use val_loss emitted during training and
minimize it. Standalone evaluation metrics are checkpoint-validation KPIs,
not the recommendation-selection objective.
Evaluation checkpoint metrics:[segm] test_mAP50 and
[bbox] test_mAP50 when the checkpoint produces class predictions. A very
short smoke-trained checkpoint can complete evaluation successfully with an
empty KPI dictionary; verify the evaluation action and result artifact in
that case. Train-stage AutoML selection remains based on val_loss.
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.
Optional. Validation uses COCO-format annotations even when training uses ODVG.
Important Parameters
model.backbone: Default swin_tiny_224_1k. Same backbone options as Grounding DINO.
train.optim.lr: Learning rate. Default 2e-4. lr_backbone 2e-5. Reuses GDINOTrainExpConfig — same training setup as Grounding DINO.
model.num_queries: Object queries. Default 900.
model.enc_layers / model.dec_layers: Keep both at 6 for train/AutoML
runs. The mask head asserts six decoder outputs during validation, so
copying Grounding DINO smoke overrides that reduce transformer layers causes
an immediate failure.
AutoML metric note: Use metric="val_loss" with
direction="minimize" for train-stage AutoML. The packaged train loop logs
validation loss scalars; it does not emit [bbox] val_mAP@50 during the
train job. Standalone evaluation emits [segm] test_mAP50 and
[bbox] test_mAP50 only when predictions survive its thresholds; do not
substitute either conditional test metric for the training loss used to
rank AutoML recommendations.
evaluate.text_threshold: Text matching threshold. Default 0.3.
dataset.has_mask: Dataset includes mask annotations. Default True. val_data_sources default data_type is "VG".
Multi-GPU / Multi-Node
Launch method: Lightning-managed. Same DDP/FSDP behavior as Grounding DINO.
Spec Key
Description
Default
train.num_gpus
Number of GPUs
1
train.gpu_ids
GPU device indices
[0]
train.num_nodes
Number of nodes
1
train.distributed_strategy
ddp or fsdp
ddp
Hardware
Minimum 1 GPU(s), recommended 4 GPU(s). 24GB+ (A100 recommended) VRAM per GPU. Heavier than Grounding DINO due to mask prediction head. 24GB+ GPU memory recommended.
Error Patterns
CUDA out of memory: Reduce batch_size. Mask prediction adds overhead on top of Grounding DINO.
Deploy schema error for test_threshold: TAO Deploy uses
evaluate.text_threshold and inference.text_threshold. Do not use
test_threshold in deploy specs.
Deploy model shape mismatch: Carry transformer and mask structure fields
from export into deploy evaluate/inference specs, including model.num_queries,
model.num_select, model.max_text_len, model.num_region_queries, and
model.has_mask. These values must match the ONNX model used to build the
TensorRT engine.
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 mask_grounding_dino.config.json:
Action
Spec Field
Inference Function
Meaning
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.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 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.
When selecting a Mask Grounding DINO checkpoint outside the SDK resolver, match
the intended epoch/step artifact exactly, for example
model_epoch_000_step_00049.pth. The mask_gdino_model_latest.pth symlink is
valid only when latest is explicitly requested. The parent PyTorch
mask_grounding_dino CLI supports train, evaluate, inference, export,
and quantize; run TensorRT engine generation, TensorRT inference, and
TensorRT evaluation through references/tao-deploy-mask-grounding-dino.md.
