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name: office-benchmarks description: "Routes LibMTL's Office-31 and Office-Home multi-input classification workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

office-benchmarks

Use this sub-skill for the Office-31 and Office-Home benchmark workflows.

Covers

• Office-31 multi-input classification.
• Office-Home multi-input classification.
• The bundled office split layout and dataloader recipe.
• A self-contained Office runtime package under scripts/office_runtime/.
• An unpackaged launcher at scripts/main.py plus the install entry point scripts/install_office_runtime.py.
• ResNet-18 based encoders and task-specific linear decoders.

Does not cover

• NYUv2 or Cityscapes dense prediction.
• QM9 graph regression.
• PAWS-X multilingual text classification.
• Core API or custom method development unless the question is specifically about the office benchmark wiring.

When to use this sub-skill

Choose this route when the user asks things like:

• "How do I train Office-31?"
• "What is the Office-Home layout?"
• "Why must multi_input be true here?"
• "How do the office dataloaders map tasks to domains?"
• "How do I validate the split files?"

Read next

• ../../references/configuration.md for the shared flags and method kwargs.
• ../../references/troubleshooting.md for cross-cutting install and runtime failures.
• references/workflows.md for the command pattern.
• references/task-contracts.md for the task dictionary, loader, and encoder/decoder shapes.
• references/data-layouts.md for the expected split and image trees.
• references/troubleshooting.md for office-specific failures.

Workflow

1. Confirm the dataset family: office-31 or office-home.
2. Confirm that the user expects multi-input classification.
3. Validate the split text files and the image root.
4. Run scripts/check_office_data.py to confirm the bundled split files and image root before training.
5. Install the self-contained runtime package with scripts/install_office_runtime.py when you need the console entry point.
6. Launch the benchmark through scripts/main.py or the installed libmtl-office entry point, not through an external checkout.
7. Allow the pretrained resnet18 backbone to download or use cache.

Critical constraints

• multi_input must be True.
• The bundled runtime builds one dataloader per domain/task from its own split files.
• The task list depends on the dataset family:
  • Office-31: amazon, dslr, webcam
  • Office-Home: Art, Clipart, Product, Real_World

Exit criteria

Leave this sub-skill when the user has the correct dataset family, split files, run command, and failure-recovery notes for the office benchmark.