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---
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)
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