
# 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.
{"license": "bigscience-openrail-m"}
license:bigscience-openrail-m region:us
TAGS
#license-bigscience-openrail-m #region-us 


# 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.
# 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.
TAGS
#license-bigscience-openrail-m #region-us 
 # 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.
TAGS
#license-bigscience-openrail-m #region-us 
# 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.