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datasets: |
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- ILSVRC/imagenet-1k |
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pipeline_tag: unconditional-image-generation |
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--- |
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# Model Card for ImageNet 32x32 R3GAN Model |
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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 |
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## Model Details |
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The model achieves 1.27 Frechet Inception Distance-50k on ImageNet64x64 class conditional ImgNet generation. |
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### Model Description |
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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. |
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- **Developed by:** Brown University and Cornell University |
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- **Funded by:** National Science Foundation and National Institute of Health (See paper for funding details) |
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- **Shared by:** [Optional: Specify sharer if different from developer] |
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- **Model type:** Generative Adversarial Network |
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- **Language(s) (NLP):** N/A |
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- **License:** [Specify License, e.g., MIT, Apache 2.0, or a custom license] |
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- **Finetuned from model:** N/A |
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### Model Sources |
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- **Repository:** https://github.com/brownvc/R3GAN/ |
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- **Paper:** https://openreview.net/forum?id=OrtN9hPP7V |
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- **Demo:** [Optional: Provide a link to a demo or example usage] |
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## Uses |
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### Direct Use |
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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. |
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### Downstream Use |
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The model can be fine-tuned for specific subsets of the ImageNet dataset or other similar datasets for domain-specific image generation tasks. |
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### Out-of-Scope Use |
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The model should not be used for generating deceptive or misleading content, malicious purposes, or tasks where realistic image synthesis could cause harm. |
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## Bias, Risks, and Limitations |
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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. |
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### Recommendations |
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- Avoid using the model for sensitive applications without thorough bias evaluation. |
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- Ensure appropriate credit is given when publishing or sharing generated images. |
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## How to Get Started with the Model |
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Below is an example of how to use the model for image generation: |
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- Will add later |
