Initial ReadMe Upload

README.md

@@ -0,0 +1,59 @@
+---
+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 submission of the paper.
+
+## 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