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README.md

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+---
+license: mit
+tags:
+- neurocuda
+- spiking-neural-network
+- snn
+- neuromorphic
+- vision
+- vision
+- cifar-10
+- strongcnn
+- classification
+- medium
+pipeline_tag: image-classification
+---
+
+# neurocuda/strongcnn-cifar10-snn ⚠️
+
+7-layer CNN SNN for CIFAR-10. Gap of 6% with conversion + fine-tuning. Larger gap than ResNet-18 due to higher non-linearity. Good for studying conversion challenges in medium-depth networks.
+
+## Model Details
+
+- **Task:** image-classification
+- **Dataset:** CIFAR-10
+- **Architecture:** StrongCNN (7-layer, BatchNorm, wider channels)
+- **Training:** ANN → QCFS → IF + BPTT FT (conversion + fine-tune)
+- **Status:** beta
+
+## Performance
+
+- **SNN Accuracy:** 74.3% | **Gap:** +6.00% (within ANN)
+- **Parameters:** 4,800,000 (18.3 MB)
+- **Timesteps:** T=16
+
+## Usage
+
+```python
+import neurocuda as nc
+
+# Load the pre-converted spiking model
+snn, info = nc.hub.load("neurocuda/strongcnn-cifar10-snn")
+
+# The model is already spiking — binary IF/LIF spikes, stateful membrane
+snn.eval()
+
+# 4D input (single frame)
+import torch
+x = torch.randn(1, 2, 34, 34)  # Adjust channels/size for your model
+output = snn(x)
+
+# 5D input (temporal — event cameras, video)
+x5 = torch.randn(2, 16, 2, 34, 34)  # (Batch, Timesteps, Channels, H, W)
+output5 = snn(x5)
+```
+
+## Hardware Compatibility
+
+- **Validated on:** GPU, CPU
+- **NIR Export:** Yes — deployable to Loihi 2, SpiNNaker, FPGA
+
+## Conversion Method
+
+ANN → QCFS → IF + BPTT FT (conversion + fine-tune)
+
+## Citation
+
+```bibtex
+@software{neurocuda2026,
+  title    = {NeuroCUDA: A PyTorch-to-Neuromorphic Compiler},
+  author   = {Krishna Varma},
+  year     = {2026},
+  url      = {https://github.com/neurocuda/neurocuda}
+}
+```
+
+## Limitations
+
+⚠️ Single seed result. Multi-seed verification pending.