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README.md

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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)
+