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.