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- split: train
path: I2E-Mini-ImageNet/train-*
license: mit
task_categories:
- image-classification
- video-classification
tags:
- neuromorphic
- snn
- spiking neural networks
- event
- dvs
- biology
- pytorch
- imagenet
- cifar10
- cifar100
- caltech101
- caltech256
- mnist
- fashionmnist
- mini-imagenet
pretty_name: I2E Neuromorphic Dataset
language:
- en
---
I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks
[](https://arxiv.org/abs/2511.08065)
[](https://aaai.org/)
[](https://scholar.google.com/scholar?cluster=1814482600796011970)
[](https://github.com/Ruichen0424/I2E)
[](https://huggingface.co/papers/2511.08065)
[](https://huggingface.co/Ruichen0424/I2E)
## 🚀 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](./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.
```python
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.
```python
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](https://github.com/Ruichen0424/I2E).*
[](https://github.com/Ruichen0424/I2E)
[](https://huggingface.co/Ruichen0424/I2E)
## 📜 Citation
If you find this work or the models useful, please cite our AAAI 2026 paper:
```bibtex
@article{ma2025i2e,
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},
journal={arXiv preprint arXiv:2511.08065},
year={2025}
}
``` 
