qriton/hlm-vision-mix-cifar10

HLM-Vision-Mix - CIFAR-10

HLM-Vision-Mix is a compact image-classification research model that combines a convolutional stem, a 2D spatial mixer, polynomial-Hopfield blocks, and mean pooling.

This repository stages two CIFAR-10 variants:

Variant	Parameters	Reported validation accuracy	File
Small	4.35M	78.6%	small_model.pt
Large	22.57M	83.9%	large_model.pt

These are not state-of-the-art CIFAR-10 models. The value of the release is the architecture/probe record: the spatial mixer fixed an earlier patch-only failure mode, and patch-shuffle probes were substantially more damaging than color-shuffle probes in the small model.

Files

File	Purpose
small_model.pt	Sanitized model-only checkpoint for the 4.35M variant
large_model.pt	Sanitized model-only checkpoint for the 22.57M variant
config.json	Public architecture and metrics metadata
small_training_meta.json	Sanitized training metadata for the small variant
large_training_meta.json	Sanitized training metadata for the large variant
small_probe_v4.json	Small-model perturbation probe summary

Intended Use

• Research on HLM-style vision blocks.
• Architecture and perturbation-probe comparisons.
• Educational CIFAR-10 experiments.

Limitations

• Accuracy is below standard CIFAR-10 CNN/ResNet recipes with modern augmentation.
• No robustness, safety, or production-vision claim.
• The probe results support a research hypothesis; they are not a full vision benchmark suite.