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name: vision-data-models description: "Operate TLLib vision datasets, ImageList formats, transforms, model factories, re-id metrics, and shared utilities." disable-model-invocation: true metadata: disco-role: operating license: MIT

Vision Data and Models

Use this sub-skill when a task needs TLLib's vision data/model layer rather than an algorithm-specific training recipe. It covers dataset classes, local image-list formats, classification/segmentation/keypoint/re-id/object-detection model surfaces, transforms, metrics, data iterators, and logging helpers.

Route here for

• Building or validating a local ImageList dataset for domain adaptation, domain generalization, task adaptation, or semi-supervised learning workflows.
• Choosing a TLLib dataset wrapper such as Office31, OfficeHome, DomainNet, VisDA2017, PACS, digit datasets, natural object recognition datasets, segmentation datasets, keypoint datasets, regression datasets, or re-identification datasets.
• Instantiating no-download model factories such as resnet18/resnet50, lenet, dtn, deeplabv2_resnet101, pose_resnet101, and re-id ResNet/ReIdentifier components.
• Using TLLib transforms, meters, ForeverDataIterator, CombineDataset, classification/segmentation accuracy helpers, confusion matrices, keypoint metrics, or re-id CMC/mAP utilities.
• Diagnosing data-list, class-index, image-size/channel, pretrained-weight, Torch/TorchVision, object-detection, or re-id dependency failures.

Do not use this sub-skill for

• DANN/CDAN/MK-MMD/MDD/CORAL/OSBP/MCC or other domain-adaptation losses and workflows; route to domain-adaptation after data/model setup.
• MixStyle/StochNorm/IBN/L2-SP/DELTA/BSS/GroupDRO/domain-generalization/fine-tuning workflows; route to task-generalization after data/model setup.
• Pseudo-label, Mean Teacher, UDA, FixMatch, FlexMatch, or other self-training losses; route to self-training after data/model setup.
• Transferability ranking metrics such as H-score, LEEP, NCE, LogME, or TransRate; route to model-selection.
• CycleGAN/FDA/SPGAN/CyCADA image translation internals; route to translation.

Operating sequence

1. Identify the task family and whether the user has local data, external data to obtain under its own license, or only a synthetic/smoke fixture.
2. For local classification-style data, validate the image-list file with scripts/validate_imagelist.py before wiring it into a training command.
3. Choose data transforms and model factories from references/vision-data-and-models.md; avoid pretrained downloads unless the user explicitly allows network/cache use.
4. For re-identification, use references/reid-and-metrics.md for dataset tuple conventions, model/loss surfaces, and CMC/mAP behavior.
5. For failures, start with references/troubleshooting.md before changing dependencies or dataset layout.
6. For an import/model/data smoke check in an installed environment, run scripts/tllib_vision_smoke.py.

Bundled references

• references/datasets-and-formats.md - dataset classes, no-download policy, ImageList/MultipleDomainsDataset/SegmentationList/re-id/keypoint/regression format contracts.
• references/vision-data-and-models.md - transforms, model factories, task-specific model surfaces, utilities, meters, loggers, metrics.
• references/reid-and-metrics.md - person re-id datasets, samplers, models, losses, CMC/mAP/re-ranking, validation conventions.
• references/troubleshooting.md - broken links, licenses, path/label mistakes, TorchVision compatibility, pretrained downloads, image shape/channel issues, optional dependency problems.

Bundled scripts

• scripts/validate_imagelist.py: safe local ImageList format validator. It does not download data and can create a TLLib ImageList object from an installed tllib package.
• scripts/tllib_vision_smoke.py: no-network CPU smoke for installed tllib vision datasets, transforms, models, metrics, and utility imports. Object-detection imports are reported as optional if Detectron2 is unavailable.