---
license: mit
task_categories:
- image-classification
tags:
- computer-vision
- long-tail-classification
- class-imbalance
- federated-learning
- cifar-10
size_categories:
- 10K<n<100K
---

# CIFAR-10 Long-Tail Federated Dataset

## Dataset Description

This is a long-tailed version of CIFAR-10 designed for federated learning research. The dataset introduces class imbalance following an exponential decay distribution, making it ideal for studying long-tail classification in federated settings.

### Class Distribution (Training Set)
The training set follows a long-tail distribution with imbalance factor 100:

- **airplane** (Class 0): 5,000 samples
- **automobile** (Class 1): 2,997 samples
- **bird** (Class 2): 1,796 samples
- **cat** (Class 3): 1,077 samples
- **deer** (Class 4): 645 samples
- **dog** (Class 5): 387 samples
- **frog** (Class 6): 232 samples
- **horse** (Class 7): 139 samples
- **ship** (Class 8): 83 samples
- **truck** (Class 9): 50 samples

**Actual Imbalance Ratio**: 100.0:1 (Head:Tail)

### Dataset Statistics
- **Number of classes**: 10
- **Training samples**: 12,406 (long-tail distribution)
- **Test samples**: 10,000 (balanced distribution)
- **Image size**: 32x32 pixels
- **Channels**: 3 (RGB)
- **Head class samples**: 5,000
- **Tail class samples**: 50

## Dataset Structure

### Data Fields
- `img`: PIL Image object (32x32x3)
- `label`: Integer class label (0-9)

### Data Splits
- **Train**: 12,406 samples (long-tail distributed)
- **Test**: 10,000 samples (balanced for fair evaluation)

## Class Labels
0: airplane
1: automobile
2: bird
3: cat
4: deer
5: dog
6: frog
7: horse
8: ship
9: truck

## Usage with Flower Datasets

This dataset is optimized for federated learning research on long-tail classification:

```python
from flwr_datasets import FederatedDataset
from flwr_datasets.partitioner import IidPartitioner

# Load the dataset with IID partitioning across clients
partitioner = IidPartitioner(num_partitions=10)
fds = FederatedDataset(
    dataset="your-username/cifar10-lt-federated", 
    partitioners={"train": partitioner}
)

# Get data for a specific client
client_data = fds.load_partition(0)
print(f"Client 0 has {len(client_data)} samples")
```

### Federated Learning Scenarios

This dataset supports several FL research scenarios:

1. **Long-tail FL**: Study how federated learning handles class imbalance
2. **IID distribution**: Each client gets similar long-tail distribution  
3. **Non-IID variants**: Can be combined with other partitioners for heterogeneous settings
4. **Fairness research**: Analyze performance across head vs tail classes

## Comparison with Standard CIFAR-10

| Metric | Standard CIFAR-10 | CIFAR-10-LT (This Dataset) |
|--------|------------------|---------------------------|
| Train samples per class | 5,000 (uniform) | 5,000 → 50 (exponential decay) |
| Test samples per class | 1,000 (uniform) | 1,000 (uniform, unchanged) |
| Total train samples | 50,000 | 12,406 |
| Class distribution | Balanced | Long-tail (IF=100) |

## Research Applications

This dataset is particularly useful for:

- **Long-tail classification research**: Studying methods to handle class imbalance
- **Federated learning with imbalanced data**: Real-world FL scenarios
- **Fairness in ML**: Analyzing model performance across different class frequencies  
- **Few-shot learning**: Tail classes have limited samples
- **Meta-learning**: Learning from classes with varying sample sizes

## Example Usage

```python
from datasets import load_dataset
import matplotlib.pyplot as plt
import numpy as np

# Load the dataset
dataset = load_dataset("your-username/cifar10-lt-federated")

# Analyze class distribution
train_data = dataset["train"]
labels = train_data["label"]
class_counts = np.bincount(labels)

print("Class distribution:")
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
for i, count in enumerate(class_counts):
    print(f"{class_names[i]}: {count} samples")

# Visualize class distribution
plt.figure(figsize=(10, 6))
plt.bar(range(10), class_counts)
plt.xlabel("Class")
plt.ylabel("Number of Samples")
plt.title("CIFAR-10 Long-Tail Class Distribution")
plt.xticks(range(10), [name[:4] for name in class_names], rotation=45)
plt.yscale('log')
plt.show()

# Access a sample
sample = train_data[0]
print(f"Image shape: {np.array(sample['img']).shape}")
print(f"Label: {sample['label']} ({class_names[sample['label']]})")
```

## Performance Baselines

Due to the long-tail distribution, standard classification methods typically show:
- High accuracy on head classes (airplane: ~90%+)
- Poor accuracy on tail classes (truck: ~40-60%)
- Overall accuracy drop compared to balanced CIFAR-10

This creates opportunities for research on:
- Loss reweighting methods
- Data augmentation for tail classes
- Two-stage training approaches
- Ensemble methods

## Citation

```bibtex
@misc{cifar10-lt-federated,
  title={CIFAR-10 Long-Tail Federated Dataset},
  author={Created for Federated Long-Tail Classification Research},
  year={2024},
  url={https://huggingface.co/datasets/your-username/cifar10-lt-federated}
}
```

## Original CIFAR-10 Citation

```bibtex
@techreport{krizhevsky2009learning,
  title={Learning multiple layers of features from tiny images},
  author={Krizhevsky, Alex and Hinton, Geoffrey and others},
  year={2009},
  institution={Technical report, University of Toronto}
}
```

## License

MIT License - Same as original CIFAR-10 dataset

## Related Work

- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://arxiv.org/abs/1906.07413)
- [Long-tail Recognition by Routing Diverse Distribution-Aware Experts](https://arxiv.org/abs/2010.01809)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://arxiv.org/abs/1910.09217)