| --- |
| license: other |
| license_name: polyform-small-business-1.0.0 |
| license_link: https://polyformproject.org/licenses/small-business/1.0.0/ |
| library_name: pytorch |
| pipeline_tag: image-classification |
| tags: |
| - bio-inspired |
| - neuroscience |
| - lightweight |
| - medical-imaging |
| - edge-ai |
| - retinal-ganglion-cells |
| - fibonacci-strides |
| datasets: |
| - kvasir-v2 |
| - cifar-10 |
| - cifar-100 |
| - imagenet-100 |
| --- |
| |
| # MPKNet V6 - Bio-Inspired Visual Classification |
|
|
| A lightweight neural network inspired by the primate Lateral Geniculate Nucleus (LGN), implementing parallel **Magnocellular (M)**, **Parvocellular (P)**, and **Koniocellular (K)** pathways with Fibonacci-stride spatial sampling. |
|
|
| ## Architecture |
|
|
| MPKNet V6 uses three parallel pathways with biologically-motivated stride ratios (2:3:5): |
|
|
| - **P pathway** (stride 2): Fine detail and edges, analogous to Parvocellular neurons (~80% of LGN) |
| - **K pathway** (stride 3): Context signals that generate gating modulation, analogous to Koniocellular neurons (~10% of LGN) |
| - **M pathway** (stride 5): Global structure and coarse features, analogous to Magnocellular neurons (~10% of LGN) |
|
|
| The **K-gating mechanism** dynamically modulates P and M pathways via learned sigmoid gates, inspired by cross-stream modulation in biological vision. |
|
|
| ## Results |
|
|
| | Dataset | Classes | Accuracy | Parameters | |
| |---------|---------|----------|------------| |
| | Kvasir-v2 (GI endoscopy) | 8 | 89.2% | 0.21M | |
| | CIFAR-10 | 10 | 89.4% | 0.54M | |
| | CIFAR-100 | 100 | 58.8% | 0.22M | |
| | ImageNet-100 | 100 | 60.8% | 0.54M | |
|
|
| No pretraining. No augmentation. 161x fewer parameters than MobileNetV3-Small. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from mpknet_v6 import BinocularMPKNetV6 |
| from mpknet_components import count_params |
| |
| # Load model |
| model = BinocularMPKNetV6(num_classes=8, ch=48, use_stereo=True) |
| state_dict = torch.load("v6_kvasir_best.pth", map_location="cpu", weights_only=True) |
| model.load_state_dict(state_dict) |
| model.eval() |
| |
| # Inference |
| x = torch.randn(1, 3, 224, 224) |
| with torch.no_grad(): |
| logits = model(x) |
| pred = logits.argmax(dim=1) |
| ``` |
|
|
| ## Files |
|
|
| - `v6_kvasir_best.pth` - Trained weights (Kvasir-v2, 8 classes, 2.1MB) |
| - `mpknet_v6.py` - Model architecture |
| - `mpknet_components.py` - Shared components (RGCLayer, BinocularPreMPK, StereoDisparity, StridedMonocularBlock) |
|
|
| ## Citation |
|
|
| ``` |
| D.J. Lougen, "MPKNet: Bio-Inspired Visual Classification with Parallel LGN Pathways", 2025. |
| ``` |
|
|
| ## License |
|
|
| PolyForm Small Business License 1.0.0 - Free for organizations with less than $100K revenue, non-profits, and education. |
|
|
| ## Links |
|
|
| - [GitHub](https://github.com/DJLougen/MPKnet) |
|
|
