Generated banner for Tao Train Mask2former
Generated logo for Tao Train Mask2former

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/tao-train-mask2former/download
  2. 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
ActionSpec KeySourceFilesList?
evaluatedataset.train.typetrain_datasetscoco_panopticNo
evaluatedataset.val.typeeval_datasetcoco_panopticNo
evaluatedataset.test.typeeval_datasetcoco_panopticNo
evaluatedataset.train.img_dirtrain_datasetsimages.tar.gzNo
evaluatedataset.label_maptrain_datasetscoco_panoptic: label_map_panoptic.json; *: label_map.jsonNo
evaluatedataset.train.instance_jsontrain_datasetsannotations.jsonNo
evaluatedataset.train.panoptic_jsontrain_datasetsannotations_panoptic.jsonNo
evaluatedataset.train.panoptic_dirtrain_datasetsimages_panoptic.tar.gzNo
evaluatedataset.val.img_direval_datasetimages.tar.gzNo
evaluatedataset.val.instance_jsoneval_datasetannotations.jsonNo
evaluatedataset.val.panoptic_jsoneval_datasetannotations_panoptic.jsonNo
evaluatedataset.val.panoptic_direval_datasetimages_panoptic.tar.gzNo
evaluatedataset.test.img_direval_datasetimages.tar.gzNo
inferencedataset.train.typetrain_datasetscoco_panopticNo
inferencedataset.val.typeeval_datasetcoco_panopticNo
inferencedataset.test.typeeval_datasetcoco_panopticNo
inferencedataset.train.img_dirtrain_datasetsimages.tar.gzNo
inferencedataset.label_maptrain_datasetscoco_panoptic: label_map_panoptic.json; *: label_map.jsonNo
inferencedataset.train.instance_jsontrain_datasetsannotations.jsonNo
inferencedataset.train.panoptic_jsontrain_datasetsannotations_panoptic.jsonNo
inferencedataset.train.panoptic_dirtrain_datasetsimages_panoptic.tar.gzNo
inferencedataset.val.img_direval_datasetimages.tar.gzNo
inferencedataset.val.instance_jsoneval_datasetannotations.jsonNo
inferencedataset.val.panoptic_jsoneval_datasetannotations_panoptic.jsonNo
inferencedataset.val.panoptic_direval_datasetimages_panoptic.tar.gzNo
inferencedataset.test.img_direval_datasetimages.tar.gzNo
quantizedataset.train.typetrain_datasetscoco_panopticNo
quantizedataset.val.typeeval_datasetcoco_panopticNo
quantizedataset.test.typeeval_datasetcoco_panopticNo
quantizedataset.train.img_dirtrain_datasetsimages.tar.gzNo
quantizedataset.label_maptrain_datasetscoco_panoptic: label_map_panoptic.json; *: label_map.jsonNo
quantizedataset.train.instance_jsontrain_datasetsannotations.jsonNo
quantizedataset.train.panoptic_jsontrain_datasetsannotations_panoptic.jsonNo
quantizedataset.train.panoptic_dirtrain_datasetsimages_panoptic.tar.gzNo
quantizedataset.val.img_direval_datasetimages.tar.gzNo
quantizedataset.val.instance_jsoneval_datasetannotations.jsonNo
quantizedataset.val.panoptic_jsoneval_datasetannotations_panoptic.jsonNo
quantizedataset.val.panoptic_direval_datasetimages_panoptic.tar.gzNo
quantizedataset.test.img_direval_datasetimages.tar.gzNo
quantizedataset.quant_calibration_dataset.images_dirtrain_datasetsimages.tar.gzNo
traindataset.train.typetrain_datasetscoco_panopticNo
traindataset.val.typeeval_datasetcoco_panopticNo
traindataset.test.typeeval_datasetcoco_panopticNo
traindataset.train.img_dirtrain_datasetsimages.tar.gzNo
traindataset.label_maptrain_datasetscoco_panoptic: label_map_panoptic.json; *: label_map.jsonNo
traindataset.train.instance_jsontrain_datasetsannotations.jsonNo
traindataset.train.panoptic_jsontrain_datasetsannotations_panoptic.jsonNo
traindataset.train.panoptic_dirtrain_datasetsimages_panoptic.tar.gzNo
traindataset.val.img_direval_datasetimages.tar.gzNo
traindataset.val.instance_jsoneval_datasetannotations.jsonNo
traindataset.val.panoptic_jsoneval_datasetannotations_panoptic.jsonNo
traindataset.val.panoptic_direval_datasetimages_panoptic.tar.gzNo
traindataset.test.img_direval_datasetimages.tar.gzNo
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 KeyDescriptionDefault
train.num_gpusNumber of GPUs1
train.gpu_idsGPU device indices[0]
train.num_nodesNumber of nodes1
train.distributed_strategyddp or fsdpddp
  • 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:

ActionSpec FieldInference FunctionMeaning
evaluateencryption_keykeyencryption key
evaluateevaluate.checkpointparent_modelmodel file inferred from the parent job results folder
evaluateevaluate.trt_engineparent_modelmodel file inferred from the parent job results folder
evaluateresults_diroutput_dircurrent job results directory
exportencryption_keykeyencryption key
exportexport.checkpointparent_modelmodel file inferred from the parent job results folder
exportexport.onnx_filecreate_onnx_fileoutput ONNX path
exportresults_diroutput_dircurrent job results directory
gen_trt_engineencryption_keykeyencryption key
gen_trt_enginegen_trt_engine.onnx_fileparent_modelmodel file inferred from the parent job results folder
gen_trt_enginegen_trt_engine.trt_enginecreate_engine_fileoutput TensorRT engine path
gen_trt_engineresults_diroutput_dircurrent job results directory
inferenceencryption_keykeyencryption key
inferenceinference.checkpointparent_modelmodel file inferred from the parent job results folder
inferenceinference.trt_engineparent_modelmodel file inferred from the parent job results folder
inferenceresults_diroutput_dircurrent job results directory
quantizeencryption_keykeyencryption key
quantizequantize.model_pathparent_modelmodel file inferred from the parent job results folder
quantizeresults_diroutput_dircurrent job results directory
trainencryption_keykeyencryption key
trainmodel.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'}
trainresults_diroutput_dircurrent job results directory
traintrain.resume_training_checkpoint_pathresume_modelmodel 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

Files

LICENSE

Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

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

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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

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