Generated banner for Tao Train Oneformer
Generated logo for Tao Train Oneformer

Tao Train Oneformer

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Medium risk

From the author

OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a single architecture using task-conditioned queries. Use when training, evaluating, exporting, quantizing, or running inference for a TAO OneFormer model. Trigger phrases include "train OneFormer", "universal segmentation", "task-conditioned segmentation", "panoptic / instance / semantic in one model".
— the description by NVIDIA Corporation

AutoGineer summary

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

The tao-train-oneformer skill provides a comprehensive interface for managing the lifecycle of OneFormer models within the NVIDIA TAO ecosystem. It supports universal image segmentation, enabling users to perform panoptic, instance, and semantic segmentation using a single task-conditioned architecture. The skill handles training, evaluation, model export, quantization, and inference, with built-in support for AutoML to optimize training parameters. It includes detailed guidance on dataset requirements, multi-GPU training configurations, and deployment workflows via TensorRT.

What it does

  • Trains OneFormer models for universal image segmentation tasks.
  • Supports AutoML for automated hyperparameter optimization during training.
  • Evaluates model performance using metrics like mIoU.
  • Exports trained models to ONNX format for deployment.
  • Quantizes models to improve inference efficiency.
  • Integrates with TAO Deploy for TensorRT engine generation and optimized inference.

When to use it

  • When you need a unified model for panoptic, instance, and semantic segmentation.
  • When training a segmentation model within the NVIDIA TAO framework.
  • When you need to optimize model performance using AutoML.
  • When preparing a model for deployment on NVIDIA hardware using TensorRT.

Advantages

  • Unifies multiple segmentation tasks into a single architecture.
  • Streamlined integration with TAO AutoML for efficient training.
  • Provides clear paths for model export and TensorRT deployment.
  • Includes robust error handling and configuration guidance for complex training setups.

Usage examples

“Train a OneFormer model for universal segmentation.”
The agent initiates the training process using the specified dataset and default AutoML policy, configuring the model for panoptic, instance, and semantic segmentation.
“Export my trained OneFormer model to ONNX.”
The agent generates an ONNX file from the specified checkpoint, ensuring the model is ready for subsequent TensorRT engine generation.
“Run inference on my OneFormer model.”
The agent executes the inference pipeline using the provided checkpoint and test dataset, outputting segmentation results in the configured mode.
“Disable AutoML for my OneFormer training run.”
The agent proceeds with a manual training configuration, bypassing the AutoML optimization process as requested.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/tao-train-oneformer/download
  2. Unzip it into the cross-client skills folder of your project:

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

Documentation by NVIDIA Corporation

OneFormer

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).

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 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

  • AutoML training metric: mIoU, with direction=maximize

  • Standalone evaluation metric: test_mIoU, with direction=maximize

Per-Action Dataset Requirements
ActionSpec KeySourceFilesList?
evaluatedataset.train.imagestrain_datasetsimages.tar.gzNo
evaluatedataset.label_maptrain_datasetslabel_map.jsonNo
evaluatedataset.train.annotationstrain_datasetsannotations.jsonNo
evaluatedataset.train.panoptictrain_datasetsimages_panoptic.tar.gzNo
evaluatedataset.val.imageseval_datasetimages.tar.gzNo
evaluatedataset.val.annotationseval_datasetannotations.jsonNo
evaluatedataset.val.panopticeval_datasetimages_panoptic.tar.gzNo
evaluatedataset.test.imageseval_datasetimages.tar.gzNo
evaluatedataset.test.annotationseval_datasetannotations.jsonNo
evaluatedataset.test.panopticeval_datasetimages_panoptic.tar.gzNo
inferencedataset.train.imagestrain_datasetsimages.tar.gzNo
inferencedataset.label_maptrain_datasetslabel_map.jsonNo
inferencedataset.train.annotationstrain_datasetsannotations.jsonNo
inferencedataset.train.panoptictrain_datasetsimages_panoptic.tar.gzNo
inferencedataset.val.imageseval_datasetimages.tar.gzNo
inferencedataset.val.annotationseval_datasetannotations.jsonNo
inferencedataset.val.panopticeval_datasetimages_panoptic.tar.gzNo
inferencedataset.test.imagesinference_datasetimages.tar.gzNo
quantizedataset.train.imagestrain_datasetsimages.tar.gzNo
quantizedataset.train.annotationstrain_datasetsannotations.jsonNo
quantizedataset.label_maptrain_datasetslabel_map.jsonNo
quantizedataset.train.panoptictrain_datasetsimages_panoptic.tar.gzNo
quantizedataset.val.imageseval_datasetimages.tar.gzNo
quantizedataset.val.annotationseval_datasetannotations.jsonNo
quantizedataset.val.panopticeval_datasetimages_panoptic.tar.gzNo
quantizedataset.test.imageseval_datasetimages.tar.gzNo
quantizedataset.quant_calibration_dataset.images_dircalibration_datasetimages.tar.gzNo
traindataset.train.imagestrain_datasetsimages.tar.gzNo
traindataset.train.annotationstrain_datasetsannotations.jsonNo
traindataset.label_maptrain_datasetslabel_map.jsonNo
traindataset.train.panoptictrain_datasetsimages_panoptic.tar.gzNo
traindataset.val.imageseval_datasetimages.tar.gzNo
traindataset.val.annotationseval_datasetannotations.jsonNo
traindataset.val.panopticeval_datasetimages_panoptic.tar.gzNo
traindataset.test.imageseval_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"
S3_INFERENCE = "s3://bucket/data/inference"
S3_CALIBRATION = "s3://bucket/data/calibration"

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,
    "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):

{
    "evaluate.checkpoint": "<selected train/AutoML checkpoint>",
    "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:

{
    "export.checkpoint": "<selected train/AutoML checkpoint>",
    "model.sem_seg_head.num_classes": 133,
    "model.export": True,
    "export.onnx_file": "/results/oneformer_export_640.onnx",
}

inference (mandatory data sources):

{
    "inference.checkpoint": "<selected train/AutoML checkpoint>",
    "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_INFERENCE}/images.tar.gz",
    "inference.images_dir": f"{S3_INFERENCE}/images.tar.gz",
}

quantize (mandatory data sources):

{
    "quantize.model_path": "<selected train/AutoML checkpoint>",
    "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_CALIBRATION}/images.tar.gz",
}

Checkpoint Selection

OneFormer training writes epoch-step checkpoints such as model_epoch_000_step_00017.pth and may also write a oneformer_model_latest.pth symlink. For checkpoint-dependent actions, use the model-skill or SDK parent-model resolver and pass the exact selected checkpoint path into evaluate.checkpoint, inference.checkpoint, export.checkpoint, quantize.model_path, or train.resume_training_checkpoint_path. Do not pick the oneformer_model_latest.pth symlink by name unless the user explicitly asks for latest checkpoint behavior. If the resolver reports a best checkpoint, use that best checkpoint for evaluation/export/inference; if the user asks for a specific epoch or step, use the matching epoch-step checkpoint.

Eval Dataset

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

Important Parameters

  • model.sem_seg_head.num_classes: Number of segmentation class indices available to the head. Default 133 for COCO panoptic data when dataset.contiguous_id: True remaps raw category ids through the label map. Do not shrink this to a global workflow class count unless the label map and annotations have actually been reduced to that class set.

  • model.one_former.hidden_dim: Keep at 256 for local smoke runs unless the text encoder width is changed in lock-step. Reducing hidden_dim alone causes a text feature/context dimension mismatch during training.

  • 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 KeyDescriptionDefault
train.num_gpusNumber of GPUs1
train.gpu_idsGPU device indices[0]
train.num_nodesNumber of nodes1
  • 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.

Extracted S3 tarball points one level too high: For local Docker runs, images.tar.gz and images_panoptic.tar.gz may extract wrapper directories such as images/ and images_panoptic/. Set dataset.*.images, dataset.*.panoptic, inference.images_dir, and quantization calibration paths to the actual folder containing image or panoptic files, not the wrapper directory. A one-level-too-high path fails with FileNotFoundError for the first annotation image even though recursive file counts look correct.

default_specs missing results_dir: The CLI default_specs subtask ignores -e experiment specs for results_dir; pass a Hydra-style override instead: oneformer default_specs results_dir=/path/to/default_specs.

Invalid Lightning precision fp32: Use train.precision: "32" in train/AutoML/evaluate/inference specs. The current Lightning stack rejects the legacy fp32 string.

PyTorch 2.6 checkpoint load failure on downstream actions: Current OneFormer checkpoints include OmegaConf objects. For checkpoints produced by the same trusted TAO train/AutoML workflow, set TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 in downstream evaluate, inference, export, quantize, or resume job env vars so Lightning can load the full checkpoint. Do not use this env var for untrusted checkpoints.

CUDA device-side assert in matcher/class cost: If training fails in oneformer/utils/matcher.py while indexing out_prob[:, tgt_ids], compare the effective target ids with model.sem_seg_head.num_classes. The packaged COCO panoptic sample has 133 compact classes after dataset.contiguous_id: True remapping, so use model.sem_seg_head.num_classes: 133 even when a broader validation workflow passes a smaller generic num_classes value. Only use a smaller class count when the label map and annotations are reduced to that exact contiguous class set.

Inference returns PASS with no predictions: OneFormer prediction reads inference.images_dir, not dataset.test.images. Declare and populate inference.images_dir with the image folder or tarball for every inference run. dataset.test.images may still be useful for shared dataset context, but it does not drive the PyTorch predict dataloader.

Export output path pre-created as a directory: Do not declare export.onnx_file as a file output. The OneFormer exporter asserts that the ONNX path does not already exist, while the local runner pre-creates declared output paths. Set export.onnx_file explicitly in the spec to a non-existing file path under the mounted results tree. Keep the default 640x640 export shape for smoke validation; very small export shapes can trigger PyTorch ONNX shape-inference failures.

Quantize cannot find the training label map from an AutoML checkpoint: OneFormer Lightning checkpoints retain train-time absolute dataset paths in their saved hparams. When running downstream actions from an AutoML child checkpoint, keep the parent AutoML job directory accessible at its original /results/<job_id> path inside the action container in addition to passing the resolved checkpoint path. Otherwise quantize can fail while loading checkpoint hparams even when the current spec includes a valid dataset.label_map.

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:

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
trainresults_diroutput_dircurrent job results directory
traintrain.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'}
traintrain.pretrained_modelptm_if_no_resume_modelPTM when no resume checkpoint exists
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.

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-oneformer · 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-oneformer.zip