Hlm Vision Mix Cifar10

--- license: apache-2.0 library_name: pytorch pipeline_tag: image-classification tags: - hlm-vision - polynomial-hopfield - spatial-mixer - cifar-10 - research datasets: - 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.

Source context: 10 downloads · 0 likes · Pipeline image-classification · Library pytorch · Repo qriton/hlm-vision-mix-cifar10

Source context: 22 downloads · 0 likes · Pipeline image-classification · Library pytorch · Repo qriton/hlm-vision-mix-cifar10