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name: data description: "Routes PyTorch Metric Learning questions about packaged datasets and sampling workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Datasets and sampling

Use this sub-skill when the user wants to download one of the packaged benchmark datasets, define a dataset wrapper, or control batch composition with one of the bundled samplers.

Typical triggers

• "How do I load CUB, Cars196, iNaturalist2018, or Stanford Online Products?"
• "How do I create a class-balanced batch?"
• "How do I use HierarchicalSampler or FixedSetOfTriplets?"
• "How do I write a custom dataset for this library?"
• "Why does dataset download or split selection fail?"

In scope

• Datasets: BaseDataset, CUB, Cars196, INaturalist2018, StanfordOnlineProducts.
• Samplers: MPerClassSampler, HierarchicalSampler, TuplesToWeightsSampler, FixedSetOfTriplets.
• Tiny in-memory dataset helpers such as EmbeddingDataset when the question is about constructing toy batches or smoke tests.
• Dataset extension patterns from docs/extend/datasets.md.

Out of scope

• Choosing the loss/miner/reducer stack belongs in components.
• Trainer hooks, logging, and checkpointing belong in training.
• Retrieval metrics and nearest-neighbor search belong in evaluation.

How to use this sub-skill

1. Read references/datasets-and-samplers.md for the dataset and sampler map.
2. Run scripts/smoke_data.py when you want to confirm a batch-composition rule on toy labels without downloading a benchmark dataset.
3. Read references/troubleshooting.md when the failure mentions a missing dataset root, a bad split, or sampler batch-size constraints.
4. If the request is about how a sampler interacts with a trainer, pair this sub-skill with training after the sampling choice is clear.

Common routing decisions

• If the user needs a benchmark dataset and a split name, stay here.
• If the user only needs the retrieval metric after the dataset is loaded, route to evaluation.
• If the user only needs the loss that a sampler feeds, route to components.

Useful public facts

• BaseDataset defaults to train+test and supports transform, target_transform, and download.
• CUB, Cars196, INaturalist2018, and StanfordOnlineProducts all provide train/test/train+test style splits.
• MPerClassSampler needs enough label structure for the batch size and m.
• HierarchicalSampler expects hierarchical labels and a true batch_sampler slot.
• TuplesToWeightsSampler uses a miner and a subset of the dataset to weight the sampling probabilities.
• FixedSetOfTriplets is useful when the available supervision is already triplet-shaped.

Read next

• references/datasets-and-samplers.md for the dataset and sampler reference.
• references/troubleshooting.md for download and batch-composition failures.
• scripts/smoke_data.py for a tiny sampler smoke check.