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name: classification description: "Route and operate PaddleViT image-classification workflows across standalone model directories, shared configuration/data/train/eval surfaces, checkpoint and AMP handling, and the facial-expression Swin variant." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PaddleViT classification

Use this route for image-level classification in PaddleViT: choose a model family, validate a config and ImageNet-style data layout, build a model, load a checkpoint, evaluate, fine-tune, or decide whether AMP/distributed training is appropriate. Use it also for the repository's Swin-based ABAW facial-expression workflow.

Route the request

1. Select exactly one standalone model directory. Keep its config.py, model module, datasets.py, and main script together: PaddleViT is a collection of standalone projects, not an installable Python package.
2. Read model overview and select the family named by the config/checkpoint. If none is named, choose a baseline that meets the actual constraint (for example ViT for an explicit reference, SwinTransformer for hierarchical windows, or MobileViT/MobileOne for a mobile-oriented comparison), then state the choice.
3. Read configuration, then run the safe standalone config check with a YAML path. Its result is structural validation, not a claim that the model or dataset is usable.
4. For a no-data/no-checkpoint output-contract check, run the deterministic standalone model smoke. It defines a tiny independent Paddle classifier and never imports the checkout.
5. Use workflows for evaluation, fine-tuning, AMP, distributed operation, and facial expression. Consult troubleshooting for recovery.

Coverage

This sub-skill owns supervised classification across ViT, DeiT, SwinTransformer, VOLO, CSwin, PVTv2, BEiT, MobileViT, ViP, XCiT, PiT, HaloNet, PoolFormer, BoTNet, CvT, HVT, TopFormer, ConvNeXt, CoaT, ResT/ResTV2, MLP-Mixer, ResMLP, gMLP, FF_Only, RepMLP, CycleMLP, ConvMixer, ConvMLP, RepLKNet, MobileOne, and MAE fine-tuning/import surfaces. It preserves the same operating conventions for related catalog folders when a task names one, but requires that folder's own README/config/module before asserting a builder or CLI detail.

MAE masked pretraining is not ordinary supervised classification; this route covers its classifier fine-tuning surface and safe config/model inspection. The facial-expression variant has aligned face frames and ABAW annotations, plus all, coarse, and negative label mappings; it is not an ImageNet2012 list-file workflow.

Boundaries and links

• Do not route object detection, semantic segmentation, GAN, or DINO here.
• Do not perform generic static export, inference-engine conversion, or deployment benchmarking here; route that to ../deployment-and-operations/SKILL.md.
• Do not download data/checkpoints, launch full training, or infer benchmark accuracy from a config/smoke result.

Source-root and environment contract

Run a chosen model from its own source directory or put only that directory first on PYTHONPATH; several folders reuse bare module names such as config, datasets, and utils. Importing multiple directories in one interpreter can silently load the wrong module; use a fresh process per model. The bundled scripts deliberately do not use this source-root import convention: they are safe standalone checks.

The verified inspection environment contains PaddlePaddle GPU 2.6.2, yacs 0.1.8, and PyYAML. The repository documents older PaddlePaddle 2.1-era APIs, so a per-family source/API smoke is required before claiming current-runtime compatibility. The observed default ViT fact is vit.build_vit(config) with a [1, 1000] output on gpu:0; it is a shape/import fact only.

Report the selected source-root directory, config and data/checkpoint paths, model builder, preprocessing contract, device/AMP/distributed choice, checks run, and unresolved incompatibilities. Never include machine-specific activation commands or private environment paths in a downstream handoff.