I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks

🚀 Introduction

This repository hosts the I2E-Datasets, a comprehensive suite of neuromorphic datasets generated using the I2E (Image-to-Event) framework. This work has been accepted for Oral Presentation at AAAI 2026.

I2E bridges the data scarcity gap in Neuromorphic Computing and Spiking Neural Networks (SNNs). By simulating microsaccadic eye movements via highly parallelized convolution, I2E converts static images into high-fidelity event streams in real-time (>300x faster than prior methods).

👁️ Visualization

The following comparisons illustrate the high-fidelity conversion from static RGB images to dynamic event streams using I2E.

More visualization comparisons can be found in Visualization.md.

📦 Dataset Catalog

We provide a comprehensive collection of standard benchmarks converted into event streams via the I2E algorithm.

1. Standard Benchmarks (Classification)

Config Name	Original Source	Resolution $(H, W)$	I2E Ratio	Event Rate	Samples (Train/Val)
I2E-CIFAR10	CIFAR-10	128 x 128	0.07	5.86%	50k / 10k
I2E-CIFAR100	CIFAR-100	128 x 128	0.07	5.76%	50k / 10k
I2E-ImageNet	ILSVRC2012	224 x 224	0.12	6.66%	1.28M / 50k

2. Transfer Learning & Fine-grained

Config Name	Original Source	Resolution $(H, W)$	I2E Ratio	Event Rate	Samples
I2E-Caltech101	Caltech-101	224 x 224	0.12	6.25%	8.677k
I2E-Caltech256	Caltech-256	224 x 224	0.12	6.04%	30.607k
I2E-Mini-ImageNet	Mini-ImageNet	224 x 224	0.12	6.65%	60k

3. Small Scale / Toy

Config Name	Original Source	Resolution $(H, W)$	I2E Ratio	Event Rate	Samples
I2E-MNIST	MNIST	32 x 32	0.10	9.56%	60k / 10k
I2E-FashionMNIST	Fashion-MNIST	32 x 32	0.15	10.76%	60k / 10k

🔜 Coming Soon: Object Detection and Semantic Segmentation datasets.

🛠️ Preprocessing Protocol

To ensure reproducibility, we specify the exact data augmentation pipeline applied to the static images before I2E conversion.

The (H, W) in the code below corresponds to the "Resolution" column in the Dataset Catalog above.

from torchvision.transforms import v2

# Standard Pre-processing Pipeline used for I2E generation
transform_train = v2.Compose([
    # Ensure 3-channel RGB (crucial for grayscale datasets like MNIST)
    v2.Lambda(lambda x: x.convert('RGB')),
    v2.PILToTensor(),
    v2.Resize((H, W), interpolation=v2.InterpolationMode.BICUBIC),
    v2.ToDtype(torch.float32, scale=True),
])

💻 Usage

🚀 Quick Start

You do not need to download any extra scripts. Just copy the code below. It handles the binary unpacking (converting Parquet bytes to PyTorch Tensors) automatically.

import io
import torch
import numpy as np
from datasets import load_dataset
from torch.utils.data import Dataset, DataLoader

# ==================================================================
# 1. Core Decoding Function (Handles the binary packing)
# ==================================================================
def unpack_event_data(item, use_io=True):
    """
    Decodes the custom binary format:
    Header (8 bytes) -> Shape (T, C, H, W) -> Body (Packed Bits)
    """
    if use_io:
        with io.BytesIO(item['data']) as f:
            raw_data = np.load(f)
    else:
        raw_data = np.load(item)
        
    header_size = 4 * 2      # Parse Header (First 8 bytes for 4 uint16 shape values)
    shape_header = raw_data[:header_size].view(np.uint16)
    original_shape = tuple(shape_header) # Returns (T, C, H, W)
    
    packed_body = raw_data[header_size:]    # Parse Body & Bit-unpacking
    unpacked = np.unpackbits(packed_body)
    
    num_elements = np.prod(original_shape)  # Extract valid bits (Handle padding)
    event_flat = unpacked[:num_elements]
    event_data = event_flat.reshape(original_shape).astype(np.float32).copy()
    
    return torch.from_numpy(event_data)

# ==================================================================
# 2. Dataset Wrapper
# ==================================================================
class I2E_Dataset(Dataset):
    def __init__(self, cache_dir, config_name, split='train', transform=None, target_transform=None):
        print(f"🚀 Loading {config_name} [{split}] from Hugging Face...")
        self.ds = load_dataset('UESTC-BICS/I2E', config_name, split=split, cache_dir=cache_dir, keep_in_memory=False)
        self.transform = transform
        self.target_transform = target_transform

    def __len__(self):
        return len(self.ds)

    def __getitem__(self, idx):
        item = self.ds[idx]
        event = unpack_event_data(item)
        label = item['label']
        if self.transform:
            event = self.transform(event)
        if self.target_transform:
            label = self.target_transform(label)
        return event, label

# ==================================================================
# 3. Run Example
# ==================================================================
if __name__ == "__main__":
    import os
    os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'     # Use HF mirror server in some regions

    DATASET_NAME = 'I2E-CIFAR10'                            # Choose your config: 'I2E-CIFAR10', 'I2E-ImageNet', etc.
    MODEL_PATH = 'Your cache path here'                     # e.g., './hf_datasets_cache/'
    
    train_dataset = I2E_Dataset(MODEL_PATH, DATASET_NAME, split='train')
    val_dataset = I2E_Dataset(MODEL_PATH, DATASET_NAME, split='validation')

    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=32, persistent_workers=True)
    val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=32, persistent_workers=True)

    events, labels = next(iter(train_loader))
    print(f"✅ Loaded Batch Shape: {events.shape}") # Expect: [32, T, 2, H, W]
    print(f"✅ Labels: {labels}")

🏆 Results (SOTA)

Our I2E-pretraining sets new benchmarks for Sim-to-Real transfer on CIFAR10-DVS.

Dataset	Architecture	Method	Top-1 Acc
CIFAR10-DVS (Real)	MS-ResNet18	Baseline	65.6%
	MS-ResNet18	Transfer-I	83.1%
	MS-ResNet18	Transfer-II (Sim-to-Real)	92.5%

For full results and model weights, please visit our GitHub Repo.

📜 Citation

If you find this work or the models useful, please cite our AAAI 2026 paper:

@inproceedings{ma2026i2e,
  title={I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks},
  author={Ma, Ruichen and Meng, Liwei and Qiao, Guanchao and Ning, Ning and Liu, Yang and Hu, Shaogang},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  number={3},
  pages={1982--1990},
  year={2026}
}

🖼️ Poster