Mask2Former

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 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: segmentation
Formats: coco_panoptic, coco
Monitoring metric: mIoU

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
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.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.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.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

python

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

train (mandatory data sources):

python

{
    "train.num_gpus": 1,
    "train.num_epochs": 10,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "model.sem_seg_head.num_classes": 90,
    "dataset.contiguous_id": True,
    "dataset.train.img_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.label_map": {"coco_panoptic": f"{S3_TRAIN}/label_map_panoptic.json; *: label_map.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):

python

{
    "model.sem_seg_head.num_classes": 90,
    "dataset.contiguous_id": True,
    "dataset.train.img_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.label_map": {"coco_panoptic": f"{S3_TRAIN}/label_map_panoptic.json; *: label_map.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:

python

{
    "model.sem_seg_head.num_classes": 90,
}

inference (mandatory data sources):

python

{
    "model.sem_seg_head.num_classes": 90,
    "dataset.contiguous_id": True,
    "dataset.train.img_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.label_map": {"coco_panoptic": f"{S3_TRAIN}/label_map_panoptic.json; *: label_map.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):

python

{
    "dataset.train.img_dir": f"{S3_TRAIN}/images.tar.gz",
    "dataset.label_map": {"coco_panoptic": f"{S3_TRAIN}/label_map_panoptic.json; *: label_map.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.

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

Full TAO Deploy reference: tao-deploy-mask2former.

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.

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

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-mask2former

NPX Install

Tags

SKILL.md Content

Mask2Former

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

Hardware

Error Patterns

Spec Param / Parent Model Inference