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license: bigscience-openrail-m
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# Model Card for MonoSpace
## Model description
MonoSpace is a latent diffusion model developed for a university image synthesis project. The model is designed as a starting point for higher-order diffusion models and focuses on generating high-quality images. MonoSpace has been trained on a subset of ImageNet and LaiOn Hires dataset, showcasing its potential in computer vision tasks.
## Intended use
MonoSpace is intended for:
- Image synthesis
- Image classification
- Object detection
As a starting point, MonoSpace aims to provide a solid foundation for researchers and developers working on higher-order diffusion models and other advanced image synthesis techniques.
## Training data
The MonoSpace model was trained on a combination of image datasets:
- ImageNet (subset)
- LaiOn Hires
## Model performance
MonoSpace achieved promising results on image synthesis and classification tasks:
- ImageNet Top-1 Accuracy (subset): *83.41%*
## Limitations
The MonoSpace model has the following limitations:
- Lack of Robust Latent High Frequency: The model may not capture high-frequency details as effectively as desired.
- Limited training resources: Due to constraints on computational resources during development, the model might not be optimized to its full potential.
## Responsible AI
To ensure ethical use of MonoSpace, we encourage users to follow Hugging Face's guidelines on responsible AI practices, including fairness, transparency, and accountability.
## License
The MonoSpace model is available under the bigscience-openrail-m. |