Tao Train Mask2former

NVIDIAOfficialCreated by NVIDIA Corporation

Skill
Tool integration
Data and AI
Apache-2.0
Medium risk

From the author

Mask2Former for universal image segmentation (panoptic, instance, and semantic). Transformer-based with masked attention for high-quality segmentation results. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Mask2Former model. Trigger phrases include "train Mask2Former", "universal segmentation", "panoptic / instance / semantic segmentation", "masked-attention transformer segmenter".

— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides an interface for training, evaluating, exporting, quantizing, and running inference with Mask2Former models. It supports panoptic, instance, and semantic segmentation tasks using a transformer-based architecture with masked attention. The skill includes AutoML capabilities for training and integrates with TAO Deploy for TensorRT engine generation and execution.

What it does

Trains Mask2Former models for panoptic, instance, or semantic segmentation.
Evaluates model performance using mIoU metrics.
Exports trained models to ONNX format.
Quantizes models for optimized deployment.
Generates TensorRT engines for inference via TAO Deploy.
Supports AutoML policies for automated training optimization.

When to use it

When you need to perform universal image segmentation tasks.
When training a Mask2Former model with specific backbone configurations.
When preparing a model for deployment using TensorRT.
When you need to automate hyperparameter tuning for segmentation models.

Advantages

Enables a unified workflow for the entire model lifecycle.
Supports multiple segmentation modes within a single framework.
Integrates with TAO Deploy for hardware-accelerated inference.
Provides configurable AutoML support for training.

Usage examples

“Train a Mask2Former model for panoptic segmentation.”: The agent initiates the training process using the specified dataset and configuration, applying the default AutoML policy.
“Export my trained Mask2Former model to ONNX.”: The agent triggers the export action, generating an ONNX file based on the provided checkpoint and model configuration.
“Generate a TensorRT engine for my exported Mask2Former model.”: The agent uses the TAO Deploy workflow to convert the ONNX model into a TensorRT engine for optimized inference.
“Run inference on my test images using the Mask2Former model.”: The agent executes the inference action, processing the test dataset and returning segmentation results.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-train-mask2former/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip tao-train-mask2former.zip -d .agents/skills

Documentation by NVIDIA Corporation

Mask2Former

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).

Mask2Former for universal image segmentation (panoptic, instance, and semantic). Transformer-based with masked attention for high-quality segmentation results.

Set model.backbone.pretrained_weights for Swin backbone weights.

For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-mask2former.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: coco_panoptic, coco
Monitoring metric: mIoU
AutoML metric: Maximize emitted validation/evaluation mIoU; never substitute training loss.

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
evaluate	dataset.train.type	train_datasets	coco_panoptic	No
evaluate	dataset.val.type	eval_dataset	coco_panoptic	No
evaluate	dataset.test.type	eval_dataset	coco_panoptic	No
evaluate	dataset.train.img_dir	train_datasets	images.tar.gz	No
evaluate	dataset.label_map	train_datasets	coco_panoptic: label_map_panoptic.json; *: label_map.json	No
evaluate	dataset.train.instance_json	train_datasets	annotations.json	No
evaluate	dataset.train.panoptic_json	train_datasets	annotations_panoptic.json	No
evaluate	dataset.train.panoptic_dir	train_datasets	images_panoptic.tar.gz	No
evaluate	dataset.val.img_dir	eval_dataset	images.tar.gz	No
evaluate	dataset.val.instance_json	eval_dataset	annotations.json	No
evaluate	dataset.val.panoptic_json	eval_dataset	annotations_panoptic.json	No
evaluate	dataset.val.panoptic_dir	eval_dataset	images_panoptic.tar.gz	No
evaluate	dataset.test.img_dir	eval_dataset	images.tar.gz	No
inference	dataset.train.type	train_datasets	coco_panoptic	No
inference	dataset.val.type	eval_dataset	coco_panoptic	No
inference	dataset.test.type	eval_dataset	coco_panoptic	No
inference	dataset.train.img_dir	train_datasets	images.tar.gz	No
inference	dataset.label_map	train_datasets	coco_panoptic: label_map_panoptic.json; *: label_map.json	No
inference	dataset.train.instance_json	train_datasets	annotations.json	No
inference	dataset.train.panoptic_json	train_datasets	annotations_panoptic.json	No
inference	dataset.train.panoptic_dir	train_datasets	images_panoptic.tar.gz	No
inference	dataset.val.img_dir	eval_dataset	images.tar.gz	No
inference	dataset.val.instance_json	eval_dataset	annotations.json	No
inference	dataset.val.panoptic_json	eval_dataset	annotations_panoptic.json	No
inference	dataset.val.panoptic_dir	eval_dataset	images_panoptic.tar.gz	No
inference	dataset.test.img_dir	eval_dataset	images.tar.gz	No
quantize	dataset.train.type	train_datasets	coco_panoptic	No
quantize	dataset.val.type	eval_dataset	coco_panoptic	No
quantize	dataset.test.type	eval_dataset	coco_panoptic	No
quantize	dataset.train.img_dir	train_datasets	images.tar.gz	No
quantize	dataset.label_map	train_datasets	coco_panoptic: label_map_panoptic.json; *: label_map.json	No
quantize	dataset.train.instance_json	train_datasets	annotations.json	No
quantize	dataset.train.panoptic_json	train_datasets	annotations_panoptic.json	No
quantize	dataset.train.panoptic_dir	train_datasets	images_panoptic.tar.gz	No
quantize	dataset.val.img_dir	eval_dataset	images.tar.gz	No
quantize	dataset.val.instance_json	eval_dataset	annotations.json	No
quantize	dataset.val.panoptic_json	eval_dataset	annotations_panoptic.json	No
quantize	dataset.val.panoptic_dir	eval_dataset	images_panoptic.tar.gz	No
quantize	dataset.test.img_dir	eval_dataset	images.tar.gz	No
quantize	dataset.quant_calibration_dataset.images_dir	train_datasets	images.tar.gz	No
train	dataset.train.type	train_datasets	coco_panoptic	No
train	dataset.val.type	eval_dataset	coco_panoptic	No
train	dataset.test.type	eval_dataset	coco_panoptic	No
train	dataset.train.img_dir	train_datasets	images.tar.gz	No
train	dataset.label_map	train_datasets	coco_panoptic: label_map_panoptic.json; *: label_map.json	No
train	dataset.train.instance_json	train_datasets	annotations.json	No
train	dataset.train.panoptic_json	train_datasets	annotations_panoptic.json	No
train	dataset.train.panoptic_dir	train_datasets	images_panoptic.tar.gz	No
train	dataset.val.img_dir	eval_dataset	images.tar.gz	No
train	dataset.val.instance_json	eval_dataset	annotations.json	No
train	dataset.val.panoptic_json	eval_dataset	annotations_panoptic.json	No
train	dataset.val.panoptic_dir	eval_dataset	images_panoptic.tar.gz	No
train	dataset.test.img_dir	eval_dataset	images.tar.gz	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"

train (mandatory data sources):

{
    "train.num_gpus": 1,
    "train.num_epochs": 10,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "model.sem_seg_head.num_classes": 133,
    "dataset.contiguous_id": True,
    "dataset.train.type": "coco_panoptic",
    "dataset.val.type": "coco_panoptic",
    "dataset.test.type": "coco_panoptic",
    "dataset.train.img_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.label_map": f"{S3_TRAIN}/label_map_panoptic.json",
    "dataset.train.instance_json": f"{S3_TRAIN}/annotations.json",
    "dataset.train.panoptic_json": f"{S3_TRAIN}/annotations_panoptic.json",
    "dataset.train.panoptic_dir": f"{S3_TRAIN}/images_panoptic.tar.gz",
    "dataset.val.img_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.val.instance_json": f"{S3_EVAL}/annotations.json",
    "dataset.val.panoptic_json": f"{S3_EVAL}/annotations_panoptic.json",
    "dataset.val.panoptic_dir": f"{S3_EVAL}/images_panoptic.tar.gz",
    "dataset.test.img_dir": f"{S3_EVAL}/images.tar.gz",
}

evaluate (mandatory data sources):

{
    "evaluate.checkpoint": "<selected train/AutoML checkpoint>",
    "model.sem_seg_head.num_classes": 133,
    "dataset.contiguous_id": True,
    "dataset.train.type": "coco_panoptic",
    "dataset.val.type": "coco_panoptic",
    "dataset.test.type": "coco_panoptic",
    "dataset.train.img_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.label_map": f"{S3_TRAIN}/label_map_panoptic.json",
    "dataset.train.instance_json": f"{S3_TRAIN}/annotations.json",
    "dataset.train.panoptic_json": f"{S3_TRAIN}/annotations_panoptic.json",
    "dataset.train.panoptic_dir": f"{S3_TRAIN}/images_panoptic.tar.gz",
    "dataset.val.img_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.val.instance_json": f"{S3_EVAL}/annotations.json",
    "dataset.val.panoptic_json": f"{S3_EVAL}/annotations_panoptic.json",
    "dataset.val.panoptic_dir": f"{S3_EVAL}/images_panoptic.tar.gz",
    "dataset.test.img_dir": f"{S3_EVAL}/images.tar.gz",
}

export:

{
    "export.checkpoint": "<selected train/AutoML checkpoint>",
    "export.onnx_file": "<output ONNX path>",
    "model.sem_seg_head.num_classes": "<same value used for train>",
}

inference (mandatory data sources):

{
    "inference.checkpoint": "<selected train/AutoML checkpoint>",
    "model.sem_seg_head.num_classes": "<same value used for train>",
    "dataset.contiguous_id": True,
    "dataset.train.img_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.label_map": f"{S3_TRAIN}/label_map_panoptic.json",
    "dataset.train.instance_json": f"{S3_TRAIN}/annotations.json",
    "dataset.train.panoptic_json": f"{S3_TRAIN}/annotations_panoptic.json",
    "dataset.train.panoptic_dir": f"{S3_TRAIN}/images_panoptic.tar.gz",
    "dataset.val.img_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.val.instance_json": f"{S3_EVAL}/annotations.json",
    "dataset.val.panoptic_json": f"{S3_EVAL}/annotations_panoptic.json",
    "dataset.val.panoptic_dir": f"{S3_EVAL}/images_panoptic.tar.gz",
    "dataset.test.img_dir": f"{S3_EVAL}/images.tar.gz",
}

quantize (mandatory data sources):

{
    "quantize.model_path": "<selected train/export artifact>",
    "dataset.train.img_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.label_map": f"{S3_TRAIN}/label_map_panoptic.json",
    "dataset.train.instance_json": f"{S3_TRAIN}/annotations.json",
    "dataset.train.panoptic_json": f"{S3_TRAIN}/annotations_panoptic.json",
    "dataset.train.panoptic_dir": f"{S3_TRAIN}/images_panoptic.tar.gz",
    "dataset.val.img_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.val.instance_json": f"{S3_EVAL}/annotations.json",
    "dataset.val.panoptic_json": f"{S3_EVAL}/annotations_panoptic.json",
    "dataset.val.panoptic_dir": f"{S3_EVAL}/images_panoptic.tar.gz",
    "dataset.test.img_dir": f"{S3_EVAL}/images.tar.gz",
    "dataset.quant_calibration_dataset.images_dir": f"{S3_TRAIN}/images.tar.gz",
}

Eval Dataset

Optional. Val data sources are part of the dataset config alongside train.

Important Parameters

model.sem_seg_head.num_classes: Number of segmentation classes. Default 200. Must match your annotation categories.
model.backbone.swin.type: Swin Transformer variant. Default tiny. Options include tiny, small, base, large.
model.mode: Segmentation mode. Default panoptic. Options: panoptic, instance, semantic.
train.optim.lr: Learning rate. Default 2e-4 (AdamW).
dataset.train.batch_size: Per-GPU batch size. Default 1. Mask2Former is memory-intensive due to per-pixel predictions.
dataset.contiguous_id: If true, set model.sem_seg_head.num_classes to the number of label-map categories. If false, set model.sem_seg_head.num_classes above the maximum raw category id and keep the same setting for evaluate, inference, export, deploy, and quantize. The COCO panoptic S3 sample has 133 categories with raw ids up to 200, so raw-id validation uses num_classes: 201.

Multi-GPU / Multi-Node

Launch method: Lightning-managed (single python process, Lightning spawns workers).

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`

Same DDP/FSDP behavior as DINO (activation checkpoint aware)
FAN backbones auto-enable sync_batchnorm
fsdp forces FP16

Multi-node env vars (set by orchestrator): WORLD_SIZE, NODE_RANK, MASTER_ADDR, MASTER_PORT, NUM_GPU_PER_NODE.

Export / TRT Defaults

TRT data types: FP32, FP16 only — INT8 is NOT supported
The parent PyTorch mask2former CLI supports train, evaluate, inference, export, and quantize; run TensorRT engine generation, TensorRT inference, and TensorRT evaluation through references/tao-deploy-mask2former.md. Export semantic ONNX (model.mode: semantic) when validating TensorRT evaluation because the current deploy evaluator accepts semantic engines.
Keep export input dimensions compatible with the deploy templates. The packaged default export.input_width: 960 and export.input_height: 544 exports and builds a TensorRT engine successfully; shrinking export to tiny validation-only sizes such as 128x128 can hit a PyTorch ONNX minus_one_pos != -1 shape-inference assertion before ONNX is produced.

Hardware

Minimum 1 GPU(s), recommended 4 GPU(s). 24GB+ (A100 recommended) VRAM per GPU. Mask2Former is memory-heavy. batch_size=1 is the default for good reason. Multi-GPU recommended for reasonable training speed.

Error Patterns

CUDA out of memory: batch_size is already 1 by default. Reduce image resolution in augmentation config or use a smaller Swin variant.

Panoptic vs instance format mismatch: Ensure you provide the correct annotation format matching model.mode setting.

Deploy schema error for top-level dataset.type: TAO Deploy uses dataset.val.type and dataset.test.type. Do not put dataset.type at the top level of Mask2Former deploy specs.

Export ONNX shape assertion at very small resolution: If export fails with minus_one_pos != -1 from PyTorch ONNX shape inference, restore the template export dimensions (960x544) before retrying deploy validation. Keep training and evaluation image sizes small when needed for quick smoke tests, but do not carry those tiny dimensions into export unless the target shape has been verified.

Quantize checkpoint load error: Older PyTorch images can fail checkpoint-based mask2former quantize because the runtime quantize script passes experiment_spec to Mask2formerPlModule.load_from_checkpoint instead of the required cfg argument. Images with the quantize fix support the default torchao checkpoint flow. ONNX quantization still requires backend: modelopt.onnx, mode: static_ptq, a fixed dataset.test.target_size, and an image that includes modelopt.onnx.quantization.

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 mask2former.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.backbone.pretrained_weights`	`{'link': 'https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22k.pth', 'destination_path': '/ptm/mask2former/swin_tiny_patch4_window7_224_22k/swin_tiny_patch4_window7_224_22k.pth'}`	{'link': 'https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22k.pth', 'destination_path': '/ptm/mask2former/swin_tiny_patch4_window7_224_22k/swin_tiny_patch4_window7_224_22k.pth'}
train	`results_dir`	`output_dir`	current job results directory
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 Mask2Former checkpoint outside the SDK resolver, match the intended epoch/step artifact exactly, for example model_epoch_000_step_00100.pth. The mask2former_model_latest.pth symlink is valid only when latest is explicitly requested.

Deployment

tao-deploy-mask2former

Files

ATTRIBUTION.mdAdded by AutoGineer933 B
BENCHMARK.md7.0 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md19.5 KiB
config/skillspector-baseline.yaml834 B
evals/evals.json828 B
references/skill_info.yaml9.6 KiB
references/spec_template_deploy_evaluate.yaml709 B
references/spec_template_deploy_gen_trt_engine.yaml247 B
references/spec_template_deploy_inference.yaml709 B
references/spec_template_evaluate.yaml3.7 KiB
references/spec_template_export.yaml3.7 KiB
references/spec_template_gen_trt_engine.yaml3.8 KiB
references/spec_template_inference.yaml3.7 KiB
references/spec_template_quantize.yaml3.5 KiB
references/spec_template_train.yaml3.5 KiB
references/tao-deploy-mask2former.md5.9 KiB
references/tao-deploy-mask2former.skill_info.yaml3.6 KiB
schemas/evaluate.schema.json66.3 KiB
schemas/export.schema.json66.8 KiB
schemas/gen_trt_engine.schema.json69.3 KiB
schemas/inference.schema.json66.3 KiB
schemas/manifest.json17.2 KiB
schemas/quantize.schema.json63.7 KiB
schemas/train.schema.json63.7 KiB
skill-card.md4.5 KiB
skill.oms.sig9.4 KiB

schemas/inference.schema.json

{
  "automl_default_parameters": [
    "dataset.augmentation.test_min_size",
    "model.mask_former.hidden_dim",
    "dataset.augmentation.test_max_size",
    "model.mask_former.num_object_queries",
    "train.optim.weight_decay",
    "train.optim.backbone_multiplier",
    "model.mask_former.dec_layers",
    "dataset.augmentation.train_max_size",
    "train.optim.momentum",
    "train.optim.lr"
  ],
  "automl_disabled_parameters": [
    "quantize",
    "train.cudnn",
    "model.backbone.swin.out_features",
    "quantize.backend_kwargs",
    "train.gpu_ids",
    "model.backbone.swin.depths",
    "wandb.tags",
    "model.sem_seg_head",
    "model.backbone.swin.num_heads",
    "model.backbone",
    "model.backbone.swin.out_indices",
    "quantize.skip_names",
    "dataset.pixel_mean",
    "dataset.val.target_size",
    "train.optim.milestones",
    "evaluate",
    "inference",
    "train",
    "dataset.augmentation",
    "dataset.augmentation.train_crop_size",
    "gen_trt_engine",
    "gen_trt_engine.tensorrt.layers_precision",
    "model.backbone.efficientvit.out_indices",
    "dataset",
    "gen_trt_engine.tensorrt",
    "dataset.pixel_std",
    "quantize.layers",
    "dataset.quant_calibration_dataset",
    "model.backbone.efficientvit.out_features",
    "model.sem_seg_head.deformable_transformer_encoder_in_features",
    "dataset.train",
    "model",
    "train.freeze",
    "dataset.test.target_size",
    "model.backbone.efficientvit",
    "evaluate.gpu_ids",
    "dataset.augmentation.train_min_size",
    "model.mask_former",
    "train.optim",
    "dataset.val",
    "dataset.train.target_size",
    "model.backbone.swin",
    "export",
    "wandb",
    "inference.gpu_ids",
    "dataset.test"
  ],
  "default": {
    "dataset": {
      "augmentation": {
        "test_max_size": 640,
        "test_min_size": 640,
        "train_crop_size": [
          640,
          640
        ],
        "train_max_size": 2560,
        "train_min_size": [
          640
        ]
      },
      "contiguous_id": false,
      "label_map": "",
      "pin_memory": true,
      "pixel_mean": [
        0.485,
        0.456,
        0.406
      ],
      "pixel_std": [
        0.229,
        0.224,
        0.225
      ],
      "quant_calibration_dataset": {
        "images_dir": ""
      },
      "test": {
        "annot_file": "",
        "batch_size": 1,
        "img_dir": "",
        "instance_json": "",
        "name": "",
        "num_workers": 1,
        "panoptic_dir": "",
        "panoptic_json": "",
        "root_dir": "",
        "target_size": [],
        "type": "ade"
      },
      "train": {
        "annot_file": "",
        "batch_size": 1,
        "img_dir": "",
        "instance_json": "",
        "name": "",
        "num_workers": 1,
        "panoptic_dir": "",
        "panoptic_json": "",
        "root_dir": "",
        "target_size": [],
        "type": "ade"
      },
      "val": {
        "annot_file": "",
        "batch_size": 1,
        "img_dir": "",
        "instance_json": "",
        "name": "",
        "num_workers": 1,
        "panoptic_dir": "",
        "panoptic_json": "",
        "root_dir": "",
        "target_size": [],
        "type": "ade"
      }
    },
    "encryption_key": "",
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        "object_mask_threshold": 0.4,
        "overlap_threshold": 0.5,
        "sem_seg_head": {
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          "convs_dim": 256,
          "deformable_transformer_encoder_in_features": [
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            "res4",
            "res5"
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          "norm": "GN",
          "num_classes": 200,
          "transformer_enc_layers": 6
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      },
      "description": "Configurable parameters to construct the model for a Mask2former experiment.",
      "popular": [
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        "sem_seg_head"
      ],
      "properties": {
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            "model.backbone.efficientvit"
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          "automl_enabled": false,
          "default": {
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              "out_features": [
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                "res3",
                "res4",
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              "depths": [
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              "drop_rate": 0.0,
              "embed_dim": 96,
              "mlp_ratio": 4.0,
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              "patch_norm": true,
              "patch_size": 4,
              "pretrain_img_size": 384,
              "qkv_bias": true,
              "type": "tiny",
              "use_checkpoint": false,
              "window_size": 7
            },
            "type": "swin"
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          "properties": {
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                "model.backbone.efficientvit.out_indices",
                "model.backbone.efficientvit.out_features"
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                  "res3",
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                  3
                ],
                "pretrain_img_size": 384,
                "use_checkpoint": false
              },
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                  "default": "l0",
                  "description": "efficient vit name.",
                  "title": "efficient vit name",
                  "type": "string"
                },
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                  "default": [
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                    "res3",
                    "res4",
                    "res5"
                  ],
                  "description": "List of output feature names for swin backbone.",
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                "out_indices": {
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                  "default": [
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                    2,
                    3
                  ],
                  "description": "Output from which stages.",
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                  "type": "list"
                },
                "pretrain_img_size": {
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                  "description": "Input image size for training the pretrained model.",
                  "title": "pretrained imaeg size",
                  "type": "int"
                },
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                  "description": "Whether to use checkpointing to save memory.",
                  "title": "use checkpointing",
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                }
              },
              "title": "efficient vit",
              "type": "collection"
            },
            "pretrained_weights": {
              "default": "",
              "description": "[Optional] Path to a pretrained backbone file.",
              "title": "pretrained backbone path",
              "type": "string"
            },
            "swin": {
              "automl_disabled_parameters": [
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                "model.backbone.swin.num_heads",
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                "model.backbone.swin.out_features"
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              "automl_enabled": false,
              "default": {
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                "attn_drop_rate": 0.0,
                "depths": [
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                  2,
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                "drop_path_rate": 0.3,
                "drop_rate": 0.0,
                "embed_dim": 96,
                "mlp_ratio": 4.0,
                "num_heads": [
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                "out_indices": [
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                "patch_norm": true,
                "patch_size": 4,
                "pretrain_img_size": 384,
                "qkv_bias": true,
                "type": "tiny",
                "use_checkpoint": false,
                "window_size": 7
              },
              "properties": {
                "ape": {
                  "default": false,
                  "description": "If True, add absolute position embedding to the patch embedding.",
                  "title": "absolute position embedding",
                  "type": "bool"
                },
                "attn_drop_rate": {
                  "default": 0.0,
                  "description": "Attention dropout rate.",
                  "title": "attention dropout rate",
                  "type": "float"
                },
                "depths": {
                  "automl_enabled": false,
                  "default": [
                    2,
                    2,
                    6,
                    2
                  ],
                  "description": "Depths of each Swin Transformer stage.",
                  "title": "swin transformer depth",
                  "type": "list"
                },
                "drop_path_rate": {
                  "default": 0.3,
                  "description": "Stochastic drop rate",
                  "title": "stochastic drop rate",
                  "type": "float"
                },
                "drop_rate": {
                  "default": 0.0,
                  "description": "Dropout rate.",
                  "title": "dropout rate",
                  "type": "float"
                },
                "embed_dim": {
                  "default": 96,
                  "description": "Number of input channels.",
                  "title": "embedding dimensions",
                  "type": "int"
                },
                "mlp_ratio": {
                  "default": 4.0,
                  "description": "Ratio of mlp hidden dim to embedding dim.",
                  "title": "mlp ratio",
                  "type": "float"
                },
                "num_heads": {
                  "automl_enabled": false,
                  "default": [
                    3,
                    6,
                    12,
                    24
                  ],
                  "description": "Number of attention head of each stage.",
                  "title": "number of heads",
                  "type": "list"
                },
                "out_features": {
                  "automl_enabled": false,
                  "default": [
                    "res2",
                    "res3",
                    "res4",
                    "res5"
                  ],
                  "description": "List of output feature names for swin backbone.",
                  "title": "output features",
                  "type": "list"
                },
                "out_indices": {
                  "automl_enabled": false,
                  "default": [
                    0,
                    1,
                    2,
                    3
                  ],
                  "description": "Output from which stages.",
                  "title": "output indices",
                  "type": "list"
                },
                "patch_norm": {
                  "default": true,
                  "description": "If True, add normalization after patch embedding.",
                  "title": "patch normalization",
                  "type": "bool"
                },
                "patch_size": {
                  "default": 4,
                  "description": "Patch size for swin transformer.",
                  "title": "patch size",
                  "type": "int"
                },
                "pretrain_img_size": {
                  "default": 384,
                  "description": "Input image size for training the pretrained model.",
                  "title": "pretrained image size",
                  "type": "int"
                },
                "qk_scale": {
                  "description": "Override default qk scale of head_dim ** -0.5 if set.",
                  "title": "qk scale",
                  "type": "float"
                },
                "qkv_bias": {
                  "default": true,
                  "description": "If True, add a learnable bias to query, key, value.",
                  "title": "qkv bias",
                  "type": "bool"
                },
                "type": {
                  "default": "tiny",
                  "description": "Swin Transformer type",
                  "title": "swin transformer type",
                  "type": "string"
                },
                "use_checkpoint": {
                  "default": false,
                  "description": "Whether to use checkpointing to save memory.",
                  "title": "use checkpointing",
                  "type": "bool"
                },
                "window_size": {
                  "default": 7,
                  "description": "Window size for Swin Transformer.",
                  "title": "window size",
                  "type": "int"
                }
              },
              "title": "swin",
              "type": "collection"
            },
            "type": {
              "default": "swin",
              "description": "backbone name.",
              "title": "backbone name",
              "type": "string"
            }
          },
          "title": "backbone",
          "type": "collection"
        },
        "export": {
          "default": false,
          "description": "A flag to enable export mode.",
          "title": "export",
          "type": "bool"
        },
        "mask_former": {
          "automl_default_parameters": [
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            "model.mask_former.hidden_dim",
            "model.mask_former.dec_layers"
          ],
          "automl_enabled": false,
          "default": {
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            "dec_layers": 10,
            "deep_supervision": true,
            "dice_weight": 5.0,
            "dim_feedforward": 2048,
            "dropout": 0.0,
            "hidden_dim": 256,
            "importance_sample_ratio": 0.75,
            "mask_weight": 5.0,
            "nheads": 8,
            "no_object_weight": 0.1,
            "num_object_queries": 100,
            "oversample_ratio": 3.0,
            "pre_norm": false,
            "train_num_points": 12544
          },
          "popular": [
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            "importance_sample_ratio",
            "class_weight",
            "dice_weight",
            "mask_weight",
            "nheads",
            "num_object_queries"
          ],
          "properties": {
            "class_weight": {
              "default": 2.0,
              "description": "The relative weight of the classification error in the matching cost.",
              "maximum": Infinity,
              "minimum": 0.0,
              "popular": true,
              "title": "Class loss coefficient",
              "type": "float"
            },
            "dec_layers": {
              "automl_enabled": true,
              "default": 10,
              "description": "Numer of decoder layers in the transformer",
              "maximum": 50,
              "minimum": 1,
              "title": "decoder layers",
              "type": "int"
            },
            "deep_supervision": {
              "default": true,
              "description": "Flag to enable deep supervision.",
              "title": "deep supervision",
              "type": "bool"
            },
            "dice_weight": {
              "default": 5.0,
              "description": "The relative weight of the focal loss of the binary mask in the matching cost.",
              "maximum": Infinity,
              "minimum": 0.0,
              "popular": true,
              "title": "focal loss coefficient",
              "type": "float"
            },
            "dim_feedforward": {
              "default": 2048,
              "description": "Dimension of the feedforward network",
              "minimum": 1,
              "title": "dim feedforward",
              "type": "int"
            },
            "dropout": {
              "default": 0.0,
              "description": "The probability to drop out.",
              "maximum": 1.0,
              "minimum": 0.0,
              "title": "drop out ratio",
              "type": "float"
            },
            "hidden_dim": {
              "automl_enabled": true,
              "default": 256,
              "description": "Dimension of the hidden units.",
              "math_cond": "/ 8",
              "maximum": 1024,
              "minimum": 64,
              "popular": true,
              "type": "int"
            },
            "importance_sample_ratio": {
              "default": 0.75,
              "description": "Ratio of points that are sampled via importnace sampling.",
              "popular": true,
              "title": "importance sampling ratio",
              "type": "float"
            },
            "mask_weight": {
              "default": 5.0,
              "description": "The relative weight of the dice loss of the binary mask in the matching cost",
              "maximum": Infinity,
              "minimum": 0.0,
              "popular": true,
              "title": "mask loss coefficient",
              "type": "float"
            },
            "nheads": {
              "default": 8,
              "description": "Number of heads",
              "popular": true,
              "title": "nheads",
              "type": "int"
            },
            "no_object_weight": {
              "default": 0.1,
              "description": "The relative classification weight applied to the no-object category.",
              "title": "no object coefficient",
              "type": "float"
            },
            "num_object_queries": {
              "automl_enabled": true,
              "default": 100,
              "description": "The number of queries",
              "maximum": Infinity,
              "minimum": 1,
              "popular": true,
              "title": "number of queries",
              "type": "int"
            },
            "oversample_ratio": {
              "default": 3.0,
              "description": "Oversampling parameter.",
              "title": "oversampling ratio",
              "type": "float"
            },
            "pre_norm": {
              "default": false,
              "description": "Flag to add layer norm in the encoder or not.",
              "title": "Pre norm",
              "type": "bool"
            },
            "train_num_points": {
              "default": 12544,
              "description": "The number of points P to sample.",
              "title": "number of points",
              "type": "int"
            }
          },
          "title": "mask2former",
          "type": "collection"
        },
        "mode": {
          "default": "panoptic",
          "description": "Segmentation mode.",
          "enum": [
            "panoptic",
            "instance",
            "semantic"
          ],
          "title": "segmentation mode",
          "type": "categorical"
        },
        "object_mask_threshold": {
          "default": 0.4,
          "description": "The value of the threshold to be used when\n                    filtering out the object mask.",
          "title": "object mask threshold",
          "type": "float"
        },
        "overlap_threshold": {
          "default": 0.5,
          "description": "The value of the threshold to be used when\n                    evaluating overlap.",
          "title": "overlap threshold",
          "type": "float"
        },
        "sem_seg_head": {
          "automl_disabled_parameters": [
            "model.sem_seg_head.deformable_transformer_encoder_in_features"
          ],
          "automl_enabled": false,
          "default": {
            "common_stride": 4,
            "convs_dim": 256,
            "deformable_transformer_encoder_in_features": [
              "res3",
              "res4",
              "res5"
            ],
            "mask_dim": 256,
            "norm": "GN",
            "num_classes": 200,
            "transformer_enc_layers": 6
          },
          "popular": [
            "transformer_enc_layers",
            "convs_dim",
            "mask_dim"
          ],
          "properties": {
            "common_stride": {
              "default": 4,
              "description": "Common stride.",
              "minimum": 2,
              "title": "Common stride",
              "type": "int"
            },
            "convs_dim": {
              "default": 256,
              "description": "Convolutional layer dimension.",
              "minimum": 1,
              "popular": true,
              "title": "conv layer dim.",
              "type": "int"
            },
            "deformable_transformer_encoder_in_features": {
              "automl_enabled": false,
              "default": [
                "res3",
                "res4",
                "res5"
              ],
              "description": "List of feature names for deformable transformer encoder input.",
              "title": "transformer encoder in_features",
              "type": "list"
            },
            "mask_dim": {
              "default": 256,
              "description": "Mask head dimension.",
              "minimum": 1,
              "popular": true,
              "title": "mask head dim.",
              "type": "int"
            },
            "norm": {
              "default": "GN",
              "description": "Norm layer type.",
              "title": "norm type",
              "type": "string"
            },
            "num_classes": {
              "default": 200,
              "description": "Number of classes.",
              "minimum": 1,
              "title": "number of classes.",
              "type": "int"
            },
            "transformer_enc_layers": {
              "default": 6,
              "description": "Number of transformer encoder layers.",
              "minimum": 1,
              "popular": true,
              "title": "Number of transformer encoder layers.",
              "type": "int"
            }
          },
          "title": "head",
          "type": "collection"
        },
        "test_topk_per_image": {
          "default": 100,
          "description": " keep topk instances per image for instance segmentation.",
          "title": "top k per image",
          "type": "int"
        }
      },
      "type": "collection"
    },
    "model_name": {
      "default": "",
      "description": "Name of model if invoking task via :code:`model_agnostic`",
      "title": "Model name",
      "type": "string"
    },
    "quantize": {
      "automl_disabled_parameters": [
        "quantize.layers",
        "quantize.skip_names",
        "quantize.backend_kwargs"
      ],
      "automl_enabled": false,
      "default": {
        "algorithm": "minmax",
        "backend": "torchao",
        "backend_kwargs": {},
        "device": "cuda",
        "layers": [],
        "mode": "weight_only_ptq",
        "model_path": "",
        "results_dir": "",
        "skip_names": []
      },
      "description": "Configurable parameters to run model quantization for a Mask2Former experiment.",
      "properties": {
        "algorithm": {
          "default": "minmax",
          "description": "Calibration/optimization algorithm. Used by ModelOpt backends (modelopt.pytorch and modelopt.onnx). Ignored by torchao backend.",
          "enum": [
            "minmax",
            "max",
            "entropy",
            "awq_clip",
            "awq_lite",
            "awq_full",
            "rtn_dq"
          ],
          "title": "Calibration algorithm",
          "type": "categorical"
        },
        "backend": {
          "default": "torchao",
          "description": "The quantization backend to use",
          "enum": [
            "modelopt.pytorch",
            "torchao",
            "modelopt.onnx"
          ],
          "title": "Quantization backend",
          "type": "categorical"
        },
        "backend_kwargs": {
          "automl_enabled": false,
          "description": "Additional keyword arguments to pass to the backend",
          "title": "Backend kwargs",
          "type": "collection"
        },
        "device": {
          "default": "cuda",
          "description": "Device to use for calibration. Accepts 'cuda' (uses default GPU), 'cpu', 'trt' (TensorRT for ONNX backend), or specific GPU device like 'cuda:0', 'cuda:1', etc. If 'cuda' is specified but no GPU is available, the framework will automatically fall back to 'cpu'. Note: 'trt' is only supported by the modelopt.onnx backend for ONNX Runtime with TensorRT execution provider.",
          "pattern": "^(cuda|cpu|trt|cuda:[0-9]+)$",
          "title": "Calibration device",
          "type": "string"
        },
        "layers": {
          "automl_enabled": false,
          "default": [],
          "description": "List of per-module quantization configurations. Each entry specifies which modules to quantize and their data types. This is the primary way to configure quantization.",
          "title": "Layer quantization configs",
          "type": "list"
        },
        "mode": {
          "default": "weight_only_ptq",
          "description": "The quantization mode to use",
          "enum": [
            "static_ptq",
            "weight_only_ptq"
          ],
          "title": "Quantization mode",
          "type": "categorical"
        },
        "model_path": {
          "default": "",
          "description": "Path to the model to be quantized. For ONNX backend, path to ONNX file.",
          "title": "Model path",
          "type": "string"
        },
        "results_dir": {
          "default": "",
          "description": "Path to where all the assets generated from a task are stored.",
          "title": "Results directory",
          "type": "string"
        },
        "skip_names": {
          "automl_enabled": false,
          "default": [],
          "description": "List of module/layer names or patterns to exclude from quantization",
          "title": "Skip names",
          "type": "list"
        }
      },
      "required": [
        "model_path",
        "results_dir"
      ],
      "type": "collection"
    },
    "results_dir": {
      "default": "",
      "description": "\n        Path to where all the assets generated from a task are stored.\n        ",
      "title": "Results directory",
      "type": "string"
    },
    "train": {
      "automl_disabled_parameters": [
        "train.gpu_ids",
        "train.cudnn",
        "train.freeze",
        "train.optim"
      ],
      "automl_enabled": false,
      "default": {
        "activation_checkpoint": false,
        "checkpoint_interval": 1,
        "checkpoint_interval_unit": "epoch",
        "clip_grad_norm": 0.1,
        "clip_grad_type": "full",
        "cudnn": {
          "benchmark": false,
          "deterministic": true
        },
        "distributed_strategy": "ddp",
        "freeze": [],
        "gpu_ids": [
          0
        ],
        "is_dry_run": false,
        "num_epochs": 10,
        "num_gpus": 1,
        "num_nodes": 1,
        "optim": {
          "backbone_multiplier": 0.1,
          "gamma": 0.1,
          "lr": 0.0002,
          "lr_scheduler": "MultiStep",
          "milestones": [
            88,
            96
          ],
          "momentum": 0.9,
          "monitor_name": "train_loss",
          "type": "AdamW",
          "weight_decay": 0.05
        },
        "precision": "fp32",
        "pretrained_model_path": "",
        "results_dir": "",
        "resume_training_checkpoint_path": "",
        "seed": 1234,
        "use_distributed_sampler": false,
        "validation_interval": 1
      },
      "description": "Configurable parameters to construct the trainer for a Mask2former experiment.",
      "popular": [
        "optim",
        "validation_interval",
        "checkpoint_interval",
        "num_gpus",
        "num_nodes",
        "num_epochs",
        "gpu_ids"
      ],
      "properties": {
        "activation_checkpoint": {
          "default": false,
          "description": "\n        A True value instructs train to recompute in backward pass to save GPU memory,\n        rather than storing activations. Note: activation checkpointing is incompatible\n        with find_unused_parameters in DDP and may cause errors if the model has unused\n        parameters.",
          "title": "enable activation checkpointing",
          "type": "bool"
        },
        "checkpoint_interval": {
          "default": 1,
          "description": "The interval (in epochs) at which a checkpoint will be saved. Helps resume training.",
          "minimum": 1,
          "popular": true,
          "title": "Checkpoint interval",
          "type": "int"
        },
        "checkpoint_interval_unit": {
          "default": "epoch",
          "description": "The unit of the checkpoint interval.",
          "enum": [
            "epoch",
            "step"
          ],
          "title": "Checkpoint interval unit",
          "type": "categorical"
        },
        "clip_grad_norm": {
          "default": 0.1,
          "description": "\n        Amount to clip the gradient by L2 Norm.\n        A value of 0.0 specifies no clipping.",
          "math_cond": "> 0.0",
          "title": "clip gradient norm",
          "type": "float"
        },
        "clip_grad_type": {
          "default": "full",
          "description": "Gradient clip type.",
          "title": "clip gradient type",
          "type": "string"
        },
        "cudnn": {
          "automl_enabled": false,
          "default": {
            "benchmark": false,
            "deterministic": true
          },
          "properties": {
            "benchmark": {
              "default": false,
              "type": "bool"
            },
            "deterministic": {
              "default": true,
              "type": "bool"
            }
          },
          "type": "collection"
        },
        "distributed_strategy": {
          "default": "ddp",
          "description": "\n        The multi-GPU training strategy.\n        DDP (Distributed Data Parallel) and Fully Sharded DDP are supported.",
          "enum": [
            "ddp",
            "fsdp"
          ],
          "title": "distributed_strategy",
          "type": "categorical"
        },
        "freeze": {
          "automl_enabled": false,
          "default": [],
          "description": "\n        List of layer names to freeze.\n        Example: [\"backbone\", \"transformer.encoder\", \"input_proj\"].",
          "title": "freeze",
          "type": "list"
        },
        "gpu_ids": {
          "automl_enabled": false,
          "default": [
            0
          ],
          "description": "\n        List of GPU IDs to run the training on. The length of this list\n        must be equal to the number of gpus in train.num_gpus.",
          "popular": true,
          "title": "GPU IDs",
          "type": "list"
        },
        "is_dry_run": {
          "default": false,
          "description": "\n        Whether to run the trainer in Dry Run mode. This serves\n        as a good means to validate the spec file and run a sanity check on the trainer\n        without actually initializing and running the trainer.",
          "title": "Is dry run",
          "type": "bool"
        },
        "iters_per_epoch": {
          "description": "Number of iteration per epoch.",
          "title": "iteration per epoch",
          "type": "int"
        },
        "num_epochs": {
          "default": 10,
          "description": "Number of epochs to run the training.",
          "maximum": Infinity,
          "minimum": 1,
          "popular": true,
          "title": "Number of epochs",
          "type": "int"
        },
        "num_gpus": {
          "default": 1,
          "description": "The number of GPUs to run the train job.",
          "minimum": 1,
          "popular": true,
          "title": "Number of GPUs",
          "type": "int"
        },
        "num_nodes": {
          "default": 1,
          "description": "Number of nodes to run the training on. If > 1, then multi-node is enabled.",
          "minimum": 1,
          "popular": true,
          "title": "Number of nodes",
          "type": "int"
        },
        "optim": {
          "automl_default_parameters": [
            "train.optim.lr",
            "train.optim.backbone_multiplier",
            "train.optim.momentum",
            "train.optim.weight_decay"
          ],
          "automl_disabled_parameters": [
            "train.optim.milestones"
          ],
          "automl_enabled": false,
          "default": {
            "backbone_multiplier": 0.1,
            "gamma": 0.1,
            "lr": 0.0002,
            "lr_scheduler": "MultiStep",
            "milestones": [
              88,
              96
            ],
            "momentum": 0.9,
            "monitor_name": "train_loss",
            "type": "AdamW",
            "weight_decay": 0.05
          },
          "description": "Hyper parameters to configure the optimizer.",
          "popular": [
            "momentum",
            "weight_decay",
            "backbone_multiplier"
          ],
          "properties": {
            "backbone_multiplier": {
              "automl_enabled": true,
              "default": 0.1,
              "description": "A multiplier for backbone learning rate.",
              "math_cond": "> 0.0",
              "maximum": 1.0,
              "minimum": 0.0,
              "popular": true,
              "title": "backbone learning rate multiplier",
              "type": "float"
            },
            "gamma": {
              "default": 0.1,
              "description": "Multiplicative factor of learning rate decay.",
              "math_cond": "> 0.0",
              "title": "gamma",
              "type": "float"
            },
            "lr": {
              "automl_enabled": true,
              "default": 0.0002,
              "description": "The initial learning rate for training the model.",
              "math_cond": "> 0.0",
              "maximum": 1.0,
              "minimum": 0.0,
              "title": "learning rate",
              "type": "float"
            },
            "lr_scheduler": {
              "default": "MultiStep",
              "description": "The learning scheduler:\n                    * MultiStep : Decrease the lr by lr_decay from lr_steps\n                    * Warmuppoly : Poly learning rate schedule.",
              "enum": [
                "MultiStep",
                "Warmuppoly"
              ],
              "title": "learning rate scheduler",
              "type": "categorical"
            },
            "milestones": {
              "automl_enabled": false,
              "default": [
                88,
                96
              ],
              "description": "learning rate decay epochs.",
              "title": "learning rate decay epochs.",
              "type": "list"
            },
            "momentum": {
              "automl_enabled": true,
              "default": 0.9,
              "description": "The momentum for the AdamW optimizer.",
              "math_cond": "> 0.0",
              "maximum": 1.0,
              "minimum": 0.0,
              "popular": true,
              "title": "momentum - AdamW",
              "type": "float"
            },
            "monitor_name": {
              "default": "train_loss",
              "description": "The metric value to be monitored for the :code:`AutoReduce` Scheduler.",
              "enum": [
                "val_loss",
                "train_loss"
              ],
              "title": "monitor_name",
              "type": "categorical"
            },
            "type": {
              "default": "AdamW",
              "description": "Type of optimizer used to train the network.",
              "enum": [
                "AdamW"
              ],
              "type": "categorical"
            },
            "weight_decay": {
              "automl_enabled": true,
              "default": 0.05,
              "description": "The weight decay coefficient.",
              "math_cond": "> 0.0",
              "maximum": 1.0,
              "minimum": 0.0,
              "popular": true,
              "title": "weight decay",
              "type": "float"
            }
          },
          "title": "optimizer",
          "type": "collection"
        },
        "precision": {
          "default": "fp32",
          "description": "Precision to run the training on.",
          "enum": [
            "fp16",
            "fp32"
          ],
          "title": "precision",
          "type": "categorical"
        },
        "pretrained_model_path": {
          "default": "",
          "description": "Path to a pre-trained Mask2Former model to initialize the current training from.",
          "type": "string"
        },
        "results_dir": {
          "default": "",
          "description": "\n        Path to where all the assets generated from a task are stored.\n        ",
          "title": "Results directory",
          "type": "string"
        },
        "resume_training_checkpoint_path": {
          "default": "",
          "description": "Path to the checkpoint to resume training from.",
          "title": "Resume checkpoint path",
          "type": "string"
        },
        "seed": {
          "default": 1234,
          "description": "The seed for the initializer in PyTorch. If < 0, disable fixed seed.",
          "maximum": Infinity,
          "minimum": -1,
          "title": "Seed for randomization",
          "type": "int"
        },
        "use_distributed_sampler": {
          "default": false,
          "description": "Use distributed sampler for multi-GPU training.",
          "title": "use distributed sampler",
          "type": "bool"
        },
        "validation_interval": {
          "default": 1,
          "description": "\n        The interval (in epochs) at which a evaluation\n        will be triggered on the validation dataset.",
          "minimum": 1,
          "popular": true,
          "title": "Validation interval",
          "type": "int"
        }
      },
      "type": "collection"
    },
    "wandb": {
      "automl_disabled_parameters": [
        "wandb.tags"
      ],
      "automl_enabled": false,
      "default": {
        "enable": true,
        "entity": "",
        "group": "",
        "name": "TAO Toolkit Training",
        "project": "TAO Toolkit",
        "reinit": false,
        "run_id": "",
        "save_code": false,
        "sync_tensorboard": false,
        "tags": [
          "tao-toolkit"
        ]
      },
      "properties": {
        "enable": {
          "default": true,
          "type": "bool"
        },
        "entity": {
          "default": "",
          "type": "string"
        },
        "group": {
          "default": "",
          "type": "string"
        },
        "name": {
          "default": "TAO Toolkit Training",
          "type": "string"
        },
        "project": {
          "default": "TAO Toolkit",
          "type": "string"
        },
        "reinit": {
          "default": false,
          "type": "bool"
        },
        "run_id": {
          "default": "",
          "type": "string"
        },
        "save_code": {
          "default": false,
          "type": "bool"
        },
        "sync_tensorboard": {
          "default": false,
          "type": "bool"
        },
        "tags": {
          "automl_enabled": false,
          "default": [
            "tao-toolkit"
          ],
          "type": "list"
        }
      },
      "type": "collection"
    }
  },
  "required": [
    "quantize"
  ],
  "type": "object",
  "x_tao_schema": {
    "action": "inference",
    "core_module": "mask2former",
    "model": "mask2former",
    "network_arch": "mask2former",
    "schema_action": "inference",
    "schema_version": 1,
    "source": "tao-core dataclass config"
  }
}

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs Python, Needs Node.js

The author says: Requires docker + nvidia-container-toolkit.

Security

What our scanner noticed· Medium risk

Large base64-encoded blobs

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Attribution and licence

Created by

NVIDIA Corporation

Publisher

NVIDIA (Official)

Source

NVIDIA/skills · skills/tao-train-mask2former · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by NVIDIA Corporation or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-train-mask2former.zip