README.md

metadata

license: apache-2.0
library_name: onnxruntime
tags:
  - sleep-staging
  - knowledge-distillation
  - onnx
  - torchscript
  - edge-deployment
  - eeg
  - polysomnography
datasets:
  - sleep-edf
metrics:
  - accuracy
pipeline_tag: tabular-classification
model-index:
  - name: Conv1dStack_T2_a0.3
    results:
      - task:
          type: tabular-classification
          name: Sleep Stage Classification
        dataset:
          type: sleep-edf
          name: Sleep-EDF
        metrics:
          - type: accuracy
            value: 0.752
            name: Validation Accuracy

Conv1dStack_T2_a0.3 — Distilled Sleep Stage Classifier

A tiny (103KB, 25,957 params) sleep stage classifier distilled from SleepFM for real-time edge deployment on NVIDIA Jetson TK1 and similar constrained devices.

Model Details

Property	Value
Architecture	Conv1dStack
Parameters	25,957
Model size	103.3 KB
Distillation temperature	2
Alpha (hard label weight)	0.3
Validation accuracy	75.2%
Input shape	(B, S, 128) — pre-pooled embeddings
Output	5-class logits (Wake, REM, N1, N2, N3)
ONNX opset	11

Student Architecture Config

Conv1dStack:
  hidden_channels: 32
  kernel_size: 5

Distillation Setup

• Teacher: SleepFM (SleepEventLSTMClassifier) — biLSTM, 128-dim embeddings
• Sweep: 3 temperatures × 3 alphas × 3 architectures = 27 experiments
• Training: 50 epochs, AdamW lr=0.001, early stopping (patience=10)
• Data: Sleep-EDF (5 train / 1 val / 1 test subjects)

Target Hardware

Spec	Value
Device	NVIDIA Jetson TK1
CUDA cores	192
RAM	2 GB
Compute capability	3.2

Usage

ONNX Runtime

import numpy as np
import onnxruntime as ort

session = ort.InferenceSession("Conv1dStack_T2_a0.3.onnx")

# Input: pre-pooled embeddings (batch, seq_len, 128)
embeddings = np.random.randn(1, 120, 128).astype(np.float32)
logits = session.run(None, {"input": embeddings})[0]
predicted_stages = np.argmax(logits, axis=-1)

# Stage mapping: 0=Wake, 1=REM, 2=N1, 3=N2, 4=N3
print(predicted_stages)

TorchScript

import torch

model = torch.jit.load("Conv1dStack_T2_a0.3.pt")
embeddings = torch.randn(1, 120, 128)
logits = model(embeddings)
predicted_stages = logits.argmax(dim=-1)

Files

File	Format	Description
Conv1dStack_T2_a0.3.onnx	ONNX (opset 11)	For ONNX Runtime / TensorRT
Conv1dStack_T2_a0.3.pt	TorchScript	For PyTorch / LibTorch on-device

Limitations

• Trained on Sleep-EDF only (7 subjects) — may not generalize to other PSG datasets
• Expects pre-pooled 128-dim embeddings from SleepFM's encoder, not raw EEG
• No per-class metrics reported (overall accuracy only)
• Distilled from a single teacher checkpoint

Citation

@misc{circadia-distill-2026,
  title={Distilled Sleep Stage Classifier for Edge Deployment},
  year={2026},
  url={https://github.com/circadia}
}