Search is not available for this dataset
epoch int64 | best_accuracy float64 | train_losses list | train_accuracies list | test_losses list | test_accuracies list | learning_rates list | inference_times list |
|---|---|---|---|---|---|---|---|
8 | 38.863375 | [
4.109887604319721,
3.5409077569979046,
3.305593199686173,
3.07654918661905,
2.884803811344532,
2.655995386456131,
2.463344265561585,
2.3202100171955355
] | [
23.726137505382518,
33.47208267547008,
38.0221042055404,
42.313764891631976,
46.03129036888187,
50.19377063298407,
54.08353667288647,
57.94459595234678
] | [
3.5622999582971846,
3.4743807230676924,
3.5235972659928456,
3.4512623037610735,
3.3507276262555803,
3.3476756725992476,
3.451208463736943,
3.4018676621573314
] | [
33.12284730195178,
34.09873708381171,
34.09873708381171,
36.05051664753157,
36.79678530424799,
38.86337543053961,
37.772675086107924,
38.46153846153846
] | [
0.0005,
0.0005,
0.0005,
0.0005,
0.0005,
0.0005,
0.0005,
0.0005
] | [
258.0443024635315,
234.72118377685547,
254.5255848339626,
257.76541233062744,
253.60329662050518,
254.27237578800748,
250.55482557841708,
238.90997682298934
] |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
N-Caltech101 Event-Driven SNN Models
UWU Research Project
Team: Ramyanath, Suranjaya, Weerakon
Supervisor: Dr. K.P.P.S. Pathirana
Institution: Uva Wellassa University
Training Results
Best Model Performance
- Best Test Accuracy: 38.86%
- Final Test Accuracy: 38.46%
- Average Inference Time: 250.30ms/batch
- Training Time: 0.32 hours
- Platform: Kaggle T4 GPU
Model Architecture
- Type: 3-layer Fully-Connected SNN (Baseline)
- Parameters: 720,869
- Hidden Size: 128
- Beta (LIF): 0.9
- Temporal Steps: 150
Training Configuration
- Batch Size: 64
- Epochs: 8
- Learning Rate: 0.0005
- Weight Decay: 0.0001 (L2 regularization)
- Label Smoothing: 0.1
- Optimizer: Adam with L2 regularization
- Scheduler: ReduceLROnPlateau (patience=5)
- Mixed Precision: True
Anti-Overfitting Techniques Applied
- L2 Weight Regularization (weight_decay=0.0001)
- Label Smoothing (0.1)
- Lower Learning Rate (0.0005)
- Adaptive LR Scheduler (reduces LR when validation plateaus)
- Early Stopping (if train-test gap > 25%)
- No Dropout (as requested - using weight decay instead)
Event-Driven Processing
- Event Timestep: 2000µs (2.0ms)
- Spatial Resolution: 60x45
- Processing: Vectorized (100x faster than loop-based)
- Caching: Enabled (preprocessed tensors)
Files in This Repository
checkpoints/best_model.pth- Best performing modelcheckpoints/last_model.pth- Most recent checkpointresults/results_kaggle.json- Training metricslogs/training_history.json- Complete training historyresults/training_plots.png- Accuracy/loss curves
Usage
import torch
from model import BaselineEventSNN
# Load checkpoint
checkpoint = torch.load('checkpoints/best_model.pth')
# Initialize model
model = BaselineEventSNN(
input_size=5400,
hidden_size=128,
output_size=101,
beta=0.9
)
# Load weights
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
Deployment Target
NVIDIA Jetson Nano (ARM Architecture)
This baseline model is designed for edge deployment on neuromorphic hardware.
Last updated: 2026-01-07 11:59:59
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