| ---
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| license: apache-2.0
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| library_name: pytorch
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| pipeline_tag: other
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| tags:
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| - hlm-spatial
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| - polynomial-hopfield
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| - point-cloud-classification
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| - modelnet10
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| - 3d-perception
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| - research-baseline
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| datasets:
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| - 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 |
|
| |---|---:|
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| | Parameters | small SpatialHLM configuration |
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| | Dataset | ModelNet10 |
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| | Task | 10-class point-cloud classification |
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| | Reported validation accuracy | 83.59% |
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| | Reported validation loss | 0.9176 |
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| | Checkpoint epoch | 34 |
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| | Encoder | pointwise |
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| | Points | 1024 |
|
|
|
| ## Classes
|
|
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| `bathtub`, `bed`, `chair`, `desk`, `dresser`, `monitor`, `night_stand`, `sofa`, `table`, `toilet`.
|
|
|
| ## Checkpoint Format
|
|
|
| `model.pt` is a sanitized PyTorch checkpoint containing:
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|
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| - `model_state`: model weights
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| - `config`: public architecture and task metadata
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| - `class_names`: ModelNet10 class names
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| - `val_acc`, `val_loss`, `epoch`: reported checkpoint metrics
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|
|
| No optimizer state, training-control state, local paths, training logs, private data, or raw run metadata are included. |
|
|
| ## Intended Use
|
|
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| 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.
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|
|
| ## Limitations
|
|
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| - Baseline research checkpoint, not SOTA.
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| - Evaluated on ModelNet10-style point-cloud data.
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| - Does not claim real-world sensor robustness.
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| - Does not include a full inference package in this model repository.
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|
|
