OneFormer

OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a single architecture using task-conditioned queries.

Set train.pretrained_backbone and/or train.pretrained_model.

For TAO Deploy TensorRT actions (

gen_trt_engine

, TensorRT

evaluate

, and TensorRT

inference

), read

references/tao-deploy-oneformer.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.images	train_datasets	images.tar.gz	No
evaluate	dataset.label_map	train_datasets	label_map.json	No
evaluate	dataset.train.annotations	train_datasets	annotations.json	No
evaluate	dataset.train.panoptic	train_datasets	images_panoptic.tar.gz	No
evaluate	dataset.val.images	eval_dataset	images.tar.gz	No
evaluate	dataset.val.annotations	eval_dataset	annotations.json	No
evaluate	dataset.val.panoptic	eval_dataset	images_panoptic.tar.gz	No
evaluate	dataset.test.images	eval_dataset	images.tar.gz	No
evaluate	dataset.test.annotations	eval_dataset	annotations.json	No
evaluate	dataset.test.panoptic	eval_dataset	images_panoptic.tar.gz	No
inference	dataset.train.images	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.annotations	train_datasets	annotations.json	No
inference	dataset.train.panoptic	train_datasets	images_panoptic.tar.gz	No
inference	dataset.val.images	eval_dataset	images.tar.gz	No
inference	dataset.val.annotations	eval_dataset	annotations.json	No
inference	dataset.val.panoptic	eval_dataset	images_panoptic.tar.gz	No
inference	dataset.test.images	eval_dataset	images.tar.gz	No
quantize	dataset.train.images	train_datasets	images.tar.gz	No
quantize	dataset.train.annotations	train_datasets	annotations.json	No
quantize	dataset.label_map	train_datasets	label_map.json	No
quantize	dataset.train.panoptic	train_datasets	images_panoptic.tar.gz	No
quantize	dataset.val.images	eval_dataset	images.tar.gz	No
quantize	dataset.val.annotations	eval_dataset	annotations.json	No
quantize	dataset.val.panoptic	eval_dataset	images_panoptic.tar.gz	No
quantize	dataset.test.images	eval_dataset	images.tar.gz	No
quantize	dataset.quant_calibration_dataset.images_dir	train_datasets	images.tar.gz	No
train	dataset.train.images	train_datasets	images.tar.gz	No
train	dataset.train.annotations	train_datasets	annotations.json	No
train	dataset.label_map	train_datasets	label_map.json	No
train	dataset.train.panoptic	train_datasets	images_panoptic.tar.gz	No
train	dataset.val.images	eval_dataset	images.tar.gz	No
train	dataset.val.annotations	eval_dataset	annotations.json	No
train	dataset.val.panoptic	eval_dataset	images_panoptic.tar.gz	No
train	dataset.test.images	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": 133,
    "dataset.contiguous_id": True,
    "train.precision": "32",
    "dataset.train.images": f"{S3_TRAIN}/images.tar.gz",
    "dataset.train.annotations": f"{S3_TRAIN}/annotations.json",
    "dataset.label_map": f"{S3_TRAIN}/label_map.json",
    "dataset.train.panoptic": f"{S3_TRAIN}/images_panoptic.tar.gz",
    "dataset.val.images": f"{S3_EVAL}/images.tar.gz",
    "dataset.val.annotations": f"{S3_EVAL}/annotations.json",
    "dataset.val.panoptic": f"{S3_EVAL}/images_panoptic.tar.gz",
    "dataset.test.images": f"{S3_EVAL}/images.tar.gz",
}

evaluate (mandatory data sources):

python

{
    "model.sem_seg_head.num_classes": 133,
    "dataset.contiguous_id": True,
    "dataset.train.images": f"{S3_TRAIN}/images.tar.gz",
    "dataset.label_map": f"{S3_TRAIN}/label_map.json",
    "dataset.train.annotations": f"{S3_TRAIN}/annotations.json",
    "dataset.train.panoptic": f"{S3_TRAIN}/images_panoptic.tar.gz",
    "dataset.val.images": f"{S3_EVAL}/images.tar.gz",
    "dataset.val.annotations": f"{S3_EVAL}/annotations.json",
    "dataset.val.panoptic": f"{S3_EVAL}/images_panoptic.tar.gz",
    "dataset.test.images": f"{S3_EVAL}/images.tar.gz",
    "dataset.test.annotations": f"{S3_EVAL}/annotations.json",
    "dataset.test.panoptic": f"{S3_EVAL}/images_panoptic.tar.gz",
}

export:

python

{
    "model.sem_seg_head.num_classes": 133,
    "model.export": True,
}

inference (mandatory data sources):

python

{
    "dataset.train.images": f"{S3_TRAIN}/images.tar.gz",
    "dataset.label_map": {"coco_panoptic": f"{S3_TRAIN}/label_map_panoptic.json; *: label_map.json"},
    "dataset.train.annotations": f"{S3_TRAIN}/annotations.json",
    "dataset.train.panoptic": f"{S3_TRAIN}/images_panoptic.tar.gz",
    "dataset.val.images": f"{S3_EVAL}/images.tar.gz",
    "dataset.val.annotations": f"{S3_EVAL}/annotations.json",
    "dataset.val.panoptic": f"{S3_EVAL}/images_panoptic.tar.gz",
    "dataset.test.images": f"{S3_EVAL}/images.tar.gz",
}

quantize (mandatory data sources):

python

{
    "dataset.train.images": f"{S3_TRAIN}/images.tar.gz",
    "dataset.train.annotations": f"{S3_TRAIN}/annotations.json",
    "dataset.label_map": f"{S3_TRAIN}/label_map.json",
    "dataset.train.panoptic": f"{S3_TRAIN}/images_panoptic.tar.gz",
    "dataset.val.images": f"{S3_EVAL}/images.tar.gz",
    "dataset.val.annotations": f"{S3_EVAL}/annotations.json",
    "dataset.val.panoptic": f"{S3_EVAL}/images_panoptic.tar.gz",
    "dataset.test.images": f"{S3_EVAL}/images.tar.gz",
    "dataset.quant_calibration_dataset.images_dir": f"{S3_TRAIN}/images.tar.gz",
}

Eval Dataset

Optional. Val data configured alongside train in the dataset config.

Important Parameters

model.sem_seg_head.num_classes: Number of segmentation classes. Default 133 (COCO panoptic).
model.backbone.name: Default D2SwinTransformer (Swin-based). embed_dim=192, depths=[2,2,18,2] by default.
train.num_epochs: Default 50 — significantly higher than most TAO models. OneFormer needs more epochs for convergence.
train.optim.lr: Learning rate. Default 1e-5. Lower than Mask2Former's 2e-4.
model.task_toggling: Enable/disable specific tasks: semantic_on, instance_on, panoptic_on.
export.task: Export task mode. Options: semantic, instance, panoptic. Default semantic. Export input defaults to 640x640.
inference.mode: Inference mode. Options: semantic, instance, panoptic. Default semantic. image_size defaults to [1024, 1024].
evaluate.iou_per_class: Report per-class IoU in evaluation. Default True.

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

Uses explicit

DDPStrategy

with

find_unused_parameters=True

gradient_as_bucket_view=True

process_group_backend="nccl"

```
sync_batchnorm
```
is always enabled
No fsdp support — DDP only

Multi-node env vars (set by orchestrator):

WORLD_SIZE

NODE_RANK

MASTER_ADDR

MASTER_PORT

NUM_GPU_PER_NODE

Hardware

Minimum 2 GPU(s), recommended 4 GPU(s). 24GB+ (A100 recommended) VRAM per GPU. OneFormer is memory-intensive like Mask2Former. batch_size=1 is the default. Multi-GPU needed for reasonable training speed, especially with 50 epochs.

Error Patterns

CUDA out of memory: batch_size is already 1. Reduce image resolution or use a smaller Swin configuration.

Slow training: 50 default epochs with batch_size=1 is slow on single GPU. Use multi-GPU distributed training.

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

oneformer.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	`results_dir`	`output_dir`	current job results directory
train	`train.pretrained_backbone`	`{'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	`train.pretrained_model`	`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

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.

Deployment

tao-deploy-oneformer — OneFormer deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.

tao-train-oneformer

NPX Install

Tags

SKILL.md Content

OneFormer

Dataclass Schemas

Train Action Policy

Training Requirements

Per-Action Dataset Requirements

Typical Spec Overrides

Eval Dataset

Important Parameters

Multi-GPU / Multi-Node

Hardware

Error Patterns

Spec Param / Parent Model Inference

Deployment