ZZHENGCHAO/poolformer-classification-beta

Classification (Poolformer)

Overview

A small Poolformer implementation for Classification, packaged with an explicit configuration and an initialization checkpoint. The base variant is a reproducible starting point, not a trained model release.

Limitations

• weights are random-init, so outputs carry no signal
• robustness, fairness and domain transfer are untested
• the API may change without notice while the design settles

Evaluation Guidance

• pick a task dataset such as a task-specific labeled split
• fix a tuning budget in advance
• compare against a capacity-matched baseline
• publish raw logs next to any claim

Architecture

• Architecture: Poolformer
• Scale: base
• Attention: grouped query
• Fusion: co attention
• Activation: mish
• Normalization: groupnorm

Repository Status

• The Python file contains the model and runnable example or training entry point.
• config.json records the generated architecture settings.
• training_args.json records the default experiment recipe.
• model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint.
• No benchmark score is claimed in this repository.

Default Experiment Recipe

The generated recipe uses rmsprop with a cosine schedule.

These values exist so the script runs end-to-end. A meaningful experiment needs its own tuning budget and at least three seeds.

Files

• predict.py — primary artifact
• README.md — this documentation
• config.json — architecture configuration
• training_args.json — default experiment settings
• model.safetensors — initialization checkpoint
• USE_POLICY.md — use and redistribution notes
• NOTES.md — informal to-do notes

License

Distributed under the apache-2.0 license. External datasets keep their own terms — check them separately.