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---
datasets:
- ILSVRC/imagenet-1k
pipeline_tag: unconditional-image-generation
---
# Model Card for ImageNet 64x64 R3GAN Model
This model card provides details about the R3GAN model trained on the ImageNet dataset found in the NeurIPS 2024 paper R3GAN: https://arxiv.org/abs/2501.05441
## Model Details
The model achieves 2.09 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 |