---
license: mit
language: []
pretty_name: Augmented ImageNet Subset for Classification
dataset_type: image-classification
task_categories:
  - image-classification
size_categories:
  - 1M<n<10M
---

# Dataset Card for imagenet\_augmented

This dataset provides an **augmented version of a subset of ImageNet**, used to benchmark how classical and synthetic augmentations impact large-scale image classification models.

All training data is organized by augmentation method, and the `test/` set remains clean and unmodified. The dataset is compressed in `.zip` format and must be **unzipped before use**.

## 📥 Download & Extract

```bash
wget https://huggingface.co/datasets/ianisdev/imagenet_augmented/resolve/main/imagenet.zip
unzip imagenet.zip
```

## 📁 Dataset Structure

```bash
imagenet/
├── test/                         # Clean test images (unaltered)
└── train/
    ├── traditional/             # Color jitter, rotation, flip
    ├── mixup/                   # Interpolated image pairs
    ├── miamix/                  # Color-affine blend
    ├── auto/                    # AutoAugment (torchvision)
    ├── lsb/                     # LSB-level bit noise
    ├── gan/                     # BigGAN class-conditional samples
    ├── vqvae/                   # VQ-VAE reconstructions
    └── fusion/                  # Pairwise blended jittered samples
```

Each folder uses `ImageFolder` format:

```
train/{augmentation}/{imagenet_class}/image.jpg
test/{imagenet_class}/image.jpg
```

## Dataset Details

### Dataset Description

* **Curated by:** Muhammad Anis Ur Rahman (`@ianisdev`)
* **License:** MIT
* **Language(s):** Not applicable (visual only)

### Dataset Sources

* **Base Dataset:** [ImageNet Subset (Tiny or 1K)](https://image-net.org/)
* **VQ-VAE Model:** [ianisdev/imagenet\_vqvae](https://huggingface.co/ianisdev/imagenet_vqvae) *(if available)*

## Uses

### Direct Use

* Large-scale model training with controlled augmentation types
* Evaluating deep learning robustness at ImageNet-level complexity

### Out-of-Scope Use

* Not designed for exact ImageNet benchmarking (subset only)
* Not recommended for production model training without validation on original ImageNet

## Dataset Creation

### Curation Rationale

To study how augmentation types affect generalization in large, fine-grained image classification tasks.

### Source Data

A compressed ImageNet subset was augmented using multiple synthetic and classical pipelines.

#### Data Collection and Processing

* **Traditional**: Flip, rotate, color jitter
* **Auto**: AutoAugment (ImageNet policy)
* **Mixup, MIA Mix, Fusion**: Pairwise augmentations with affine/jitter
* **GAN**: Used pretrained [BigGAN-deep-256](https://huggingface.co/biggan-deep-256)
* **VQ-VAE**: Reconstructed using a trained encoder-decoder model

#### Who are the source data producers?

Original ImageNet images are from the official [ILSVRC](https://image-net.org/challenges/LSVRC) dataset. Augmented samples were generated by Muhammad Anis Ur Rahman.

## Bias, Risks, and Limitations

* Some classes may contain visually distorted samples
* GAN/VQ-VAE samples can introduce low-fidelity noise
* Dataset may not reflect full ImageNet diversity

### Recommendations

* Use `test/` set for consistent evaluation
* Measure class-level confusion and error propagation
* Evaluate robustness to real-world samples

## Citation

**BibTeX:**

```bash
@misc{rahman2025imagenetaug,
  author = {Muhammad Anis Ur Rahman},
  title = {Augmented ImageNet Dataset for Image Classification},
  year = {2025},
  url = {https://huggingface.co/datasets/ianisdev/imagenet_augmented}
}
```

**APA:**

Rahman, M. A. U. (2025). *Augmented ImageNet Dataset for Image Classification*. Hugging Face. [https://huggingface.co/datasets/ianisdev/imagenet\_augmented](https://huggingface.co/datasets/ianisdev/imagenet_augmented)