I2e Event Classification Eval

---
name: i2e-event-classification-eval
description: Evaluates the classification performance of Spiking Neural Networks trained on synthetic event streams generated from static images, and tests the transferability of these models to real-world neuromorphic sensor data. Use when the user wants to benchmark on I2E-CIFAR10, I2E-CIFAR100, I2E-ImageNet, CIFAR10-DVS, or asks about evaluating this task. Reports Accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2511.08065
  bibtex_key: ma2025i2e
  confidence: high
---

# i2e-event-classification-eval

> I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks — Ruichen Ma et al. (arXiv:2511.08065, 2025)

## What this evaluates

Evaluates the classification performance of Spiking Neural Networks trained on synthetic event streams generated from static images, and tests the transferability of these models to real-world neuromorphic sensor data.

## Datasets

- **I2E-CIFAR10** — total ?; splits: train (-1), test (-1)
- **I2E-CIFAR100** — total ?; splits: train (-1), test (-1)
- **I2E-ImageNet** — total ?; splits: train (-1), test (-1)
- **CIFAR10-DVS** — total ?; splits: train (-1), test (-1)

## Metrics

- `Accuracy` **(primary)** — range: percent
  - Standard classification accuracy: the proportion of correctly predicted class labels out of the total number of test instances, reported as a percentage.

## Input / output format

**Input**: Event streams represented as either dense boolean tensors or sparse coordinate lists, generated from resized static images (224x224 for ImageNet, 128x128 for CIFAR) or captured by real DVS sensors.

**Output**: Discrete class labels corresponding to the image categories.

## Scoring recipe

```python
correct = 0
for pred, gold in zip(predictions, gold_labels):
    if pred == gold:
        correct += 1
accuracy = (correct / len(gold_labels)) * 100
```

## Common pitfalls

- Confusing synthetic I2E-generated event data with real DVS sensor recordings, which have different noise and sparsity characteristics.
- Overlooking the impact of data augmentation strategies (Baseline-I vs Baseline-II) on performance, as applying standard augmentations to source images before conversion drastically boosts accuracy.
- Assuming fixed timestep counts are optimal; the paper shows performance varies significantly with timestep order and count, requiring careful ablation.

## Evidence (verbatim from paper)

> On I2E-ImageNet, MS-ResNet34 (Baseline-II) reaches 60.50% accuracy, surpassing the best prior result on other event-based ImageNet datasets by over 8%. The dramatic performance increase from Baseline-I to Baseline-II across all datasets demonstrates that I2E is not only capable of generating high-quality event data but also uniquely enables the modern training strategies required to unlock the full potential of deep SNNs.

## Citation

```bibtex
@misc{ma2025i2e,
  title={I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks},
  author={Ruichen Ma et al.},
  year={2025},
  note={arXiv:2511.08065}
}
```

- arXiv: 2511.08065