---
datasets:
- ILSVRC/imagenet-1k
pipeline_tag: unconditional-image-generation
---

# Model Card for ImageNet 32x32 R3GAN Model

This model card provides details about the R3GAN model trained on the ImageNet dataset found in the NeurIPS 2024 paper: https://arxiv.org/abs/2501.05441

## Model Details

The model achieves 1.27 Frechet Inception Distance-50k on ImageNet64x64 class conditional ImgNet generation.

### Model Description

This model is a generative adversarial network (GAN) based on the R3GAN architecture, specifically trained to synthesize high-quality and realistic images from the ImageNet dataset.

- **Developed by:** Brown University and Cornell University
- **Funded by:** National Science Foundation and National Institute of Health (See paper for funding details)
- **Shared by:** [Optional: Specify sharer if different from developer]
- **Model type:** Generative Adversarial Network
- **Language(s) (NLP):** N/A
- **License:** [Specify License, e.g., MIT, Apache 2.0, or a custom license]
- **Finetuned from model:** N/A

### Model Sources

- **Repository:** https://github.com/brownvc/R3GAN/
- **Paper:** https://openreview.net/forum?id=OrtN9hPP7V
- **Demo:** [Optional: Provide a link to a demo or example usage]

## Uses

### Direct Use

This model can be used to generate high-resolution images similar to those in the ImageNet dataset. Its primary application includes research in generative models and image synthesis.

### Downstream Use

The model can be fine-tuned for specific subsets of the ImageNet dataset or other similar datasets for domain-specific image generation tasks.

### Out-of-Scope Use

The model should not be used for generating deceptive or misleading content, malicious purposes, or tasks where realistic image synthesis could cause harm.

## Bias, Risks, and Limitations

The model inherits biases present in the ImageNet dataset, including potential overrepresentation or underrepresentation of certain classes. Users should critically evaluate and mitigate biases before deploying the model.

### Recommendations

- Avoid using the model for sensitive applications without thorough bias evaluation.
- Ensure appropriate credit is given when publishing or sharing generated images.

## How to Get Started with the Model

Below is an example of how to use the model for image generation:
- Will add later