README.md

metadata

license: apache-2.0
library_name: pytorch
pipeline_tag: other
tags:
  - hlm-spatial
  - polynomial-hopfield
  - point-cloud-classification
  - modelnet10
  - 3d-perception
  - research-baseline
datasets:
  - modelnet10

HLM-Spatial ModelNet10 Small

HLM-Spatial ModelNet10 Small is a compact polynomial-Hopfield point-cloud classifier for the ModelNet10 object classification benchmark.

This is a research baseline release, not a state-of-the-art point-cloud model. Its purpose is to make the small HLM-Spatial checkpoint inspectable and reproducible before larger follow-up spatial runs are published.

Results

Field	Value
Parameters	small SpatialHLM configuration
Dataset	ModelNet10
Task	10-class point-cloud classification
Reported validation accuracy	83.59%
Reported validation loss	0.9176
Checkpoint epoch	34
Encoder	pointwise
Points	1024

Classes

bathtub, bed, chair, desk, dresser, monitor, night_stand, sofa, table, toilet.

Checkpoint Format

model.pt is a sanitized PyTorch checkpoint containing:

• model_state: model weights
• config: public architecture and task metadata
• class_names: ModelNet10 class names
• val_acc, val_loss, epoch: reported checkpoint metrics

No optimizer state, training-control state, local paths, training logs, private data, or raw run metadata are included.

Intended Use

Use this model as a baseline for HLM-Spatial research and point-cloud classification experiments. It should not be used for safety-critical robotics, industrial inspection, medical use, or autonomous decisions.

Limitations

• Baseline research checkpoint, not SOTA.
• Evaluated on ModelNet10-style point-cloud data.
• Does not claim real-world sensor robustness.
• Does not include a full inference package in this model repository.