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name: vision-benchmarks description: "Routes LibMTL's NYUv2 and Cityscapes vision benchmark workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

vision-benchmarks

Use this sub-skill for the NYUv2 and Cityscapes image benchmark workflows.

Covers

• NYUv2 single-input dense prediction.
• Cityscapes single-input dense prediction.
• DeepLabV3+ with resnet_dilated and DeepLabHead.
• SegNet+MTAN for the NYUv2 variant.
• The shared NYUv2 helper modules for metrics, ASPP heads, SegNet+MTAN, and data loading.

Does not cover

• Office-31 / Office-Home multi-input classification.
• QM9 graph regression or PAWS-X text classification.
• Core Trainer or extensibility questions that do not depend on the vision benchmark layout.

When to use this sub-skill

Choose this route when the user asks things like:

• "How do I train NYUv2?"
• "What is the Cityscapes data layout?"
• "How do I use the SegNet+MTAN variant?"
• "What does the NYUv2 task_dict look like?"
• "Why is multi_input false for these examples?"

Read next

• ../../references/configuration.md for the shared flags and architecture kwargs.
• ../../references/troubleshooting.md for cross-cutting install and runtime failures.
• references/workflows.md for the benchmark recipes.
• references/task-contracts.md for the task dictionaries, losses, metrics, and output-channel expectations.
• references/data-layouts.md for the expected preprocessed data trees.
• references/troubleshooting.md for vision-specific failures.

Workflow

1. Confirm the dataset: NYUv2 or Cityscapes.
2. Confirm the architecture family: DeepLabV3+ or SegNet+MTAN.
3. Verify the preprocessed npy directory layout.
4. Confirm the example is being run from the correct example directory.
5. Run scripts/check_vision_data.py to validate the bundled preprocessed layout before training.
6. Check that CUDA and pretrained-weight downloads are available.

Critical constraints

• Both benchmarks are single-input problems, so multi_input must be False.
• NYUv2 uses three tasks: segmentation, depth, and surface normal.
• Cityscapes uses two tasks: segmentation and depth.
• The Cityscapes workflow reuses the NYU helper modules by adding the sibling directory to sys.path.

Exit criteria

Leave this sub-skill when the user has a complete command, data layout, and failure-recovery story for the selected vision benchmark.