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name: data-and-checkpoints description: "Use this repo skill for Swin-Transformer ImageNet folder, zip, ImageNet-22K, SimMIM data layouts, and checkpoint resume/pretrained loading behavior." disable-model-invocation: true metadata: disco-role: operating license: MIT

data-and-checkpoints

Use this sub-skill when a task involves data roots, zipped ImageNet maps, ImageNet-22K annotations, SimMIM input batches, checkpoint files, pretrained-vs-resume semantics, or class-head remapping.

What this sub-skill covers

• ImageNet-1K folder layout for torchvision.datasets.ImageFolder.
• Zipped ImageNet layout with train.zip, val.zip, train_map.txt, and val_map.txt.
• ImageNet-22K layout and JSON map expectations for IN22KDATASET.
• SimMIM pretraining data shape: images plus generated masks.
• Checkpoint dictionary expectations for load_checkpoint and load_pretrained.
• ImageNet-22K-to-1K classifier head remapping through the 22K class map.

Boundaries

• For model constructors and output tensor shapes, use core-models.
• For supervised train/eval commands, use training-eval-cli.
• For SimMIM launch commands and mask-loss details, use simmim-workflows.
• For MoE checkpoint sharding and Tutel-specific behavior, use moe-and-acceleration.

Standard workflow

1. Identify the data mode: imagenet folder, zipped imagenet, imagenet22K, or SimMIM pretrain/fine-tune.
2. Read references/data-formats.md for the exact structure.
3. Run scripts/validate_imagenet_layout.py on a small or full data root before composing a training command.
4. If a checkpoint is involved, read references/checkpoint-loading.md to choose --resume vs --pretrained and anticipate classifier-head behavior.
5. Use references/troubleshooting.md if data loaders report empty datasets, malformed maps, or checkpoint key mismatches.

Safe checks

The bundled validator checks schema and small samples only. It does not download ImageNet, rewrite validation folders, unzip archives, or load model weights.

Linked files

• references/data-formats.md - folder, zip, 22K, and SimMIM data structures.
• references/checkpoint-loading.md - resume/pretrained/checkpoint remap behavior.
• references/troubleshooting.md - common data and checkpoint failures.
• scripts/validate_imagenet_layout.py - safe schema validator for folder, zip, and ImageNet-22K JSON map layouts.