# Model Card for GANdhi

## Model description

GANdhi is a Generative Adversarial Network (GAN) based model designed to score the fairness of a dataset distribution. The model helps assess biases in datasets and provides valuable feedback to researchers and developers, ensuring a more equitable representation of data across various domains.

**The page serves as a portflio place for the project, as the training data and model weight involves classfied information.**

## Intended use
GANdhi is intended for:

- Fairness assessment in dataset distribution
- Bias detection and mitigation
- Evaluating and improving dataset quality
- The model aims to assist in creating datasets that are more balanced and representative, leading to better-performing machine learning models that are less biased.

## Training data
The GANdhi model was trained on a diverse set of datasets, which were preprocessed and curated to cover a broad range of domains and subjects. The exact datasets used in the training process are proprietary information.

## Model performance
GANdhi demonstrated effective performance in identifying biases and assessing fairness across various dataset distributions:

- Fairness scoring accuracy: 91.4% mean on ImageNet 1k
- Bias detection rate: 93.61% mean on COCO validation set

## Limitations
The GANdhi model has the following limitations:

- Sensitivity to noise: The model may be sensitive to noise present in the dataset, which could impact its fairness assessment performance.
- Incomplete domain coverage: While the model has been trained on diverse datasets, it may not cover all possible domains and contexts, leading to potential discrepancies in its performance.

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

# Model Card for GANdhi

## Model description

GANdhi is a Generative Adversarial Network (GAN) based model designed to score the fairness of a dataset distribution. The model helps assess biases in datasets and provides valuable feedback to researchers and developers, ensuring a more equitable representation of data across various domains.

The page serves as a portflio place for the project, as the training data and model weight involves classfied information.

## Intended use
GANdhi is intended for:

- Fairness assessment in dataset distribution
- Bias detection and mitigation
- Evaluating and improving dataset quality
- The model aims to assist in creating datasets that are more balanced and representative, leading to better-performing machine learning models that are less biased.

## Training data
The GANdhi model was trained on a diverse set of datasets, which were preprocessed and curated to cover a broad range of domains and subjects. The exact datasets used in the training process are proprietary information.

## Model performance
GANdhi demonstrated effective performance in identifying biases and assessing fairness across various dataset distributions:

- Fairness scoring accuracy: 91.4% mean on ImageNet 1k
- Bias detection rate: 93.61% mean on COCO validation set

## Limitations
The GANdhi model has the following limitations:

- Sensitivity to noise: The model may be sensitive to noise present in the dataset, which could impact its fairness assessment performance.
- Incomplete domain coverage: While the model has been trained on diverse datasets, it may not cover all possible domains and contexts, leading to potential discrepancies in its performance.

# Model Card for GANdhi ## Model description

GANdhi is a Generative Adversarial Network (GAN) based model designed to score the fairness of a dataset distribution. The model helps assess biases in datasets and provides valuable feedback to researchers and developers, ensuring a more equitable representation of data across various domains.

The page serves as a portflio place for the project, as the training data and model weight involves classfied information. ## Intended use
GANdhi is intended for:

- Fairness assessment in dataset distribution
- Bias detection and mitigation
- Evaluating and improving dataset quality
- The model aims to assist in creating datasets that are more balanced and representative, leading to better-performing machine learning models that are less biased. ## Training data
The GANdhi model was trained on a diverse set of datasets, which were preprocessed and curated to cover a broad range of domains and subjects. The exact datasets used in the training process are proprietary information. ## Model performance
GANdhi demonstrated effective performance in identifying biases and assessing fairness across various dataset distributions:

- Fairness scoring accuracy: 91.4% mean on ImageNet 1k
- Bias detection rate: 93.61% mean on COCO validation set ## Limitations
The GANdhi model has the following limitations:

- Sensitivity to noise: The model may be sensitive to noise present in the dataset, which could impact its fairness assessment performance.
- Incomplete domain coverage: While the model has been trained on diverse datasets, it may not cover all possible domains and contexts, leading to potential discrepancies in its performance.
TAGS
#license-bigscience-openrail-m #region-us 
 # Model Card for GANdhi ## Model description

GANdhi is a Generative Adversarial Network (GAN) based model designed to score the fairness of a dataset distribution. The model helps assess biases in datasets and provides valuable feedback to researchers and developers, ensuring a more equitable representation of data across various domains.

The page serves as a portflio place for the project, as the training data and model weight involves classfied information. ## Intended use
GANdhi is intended for:

- Fairness assessment in dataset distribution
- Bias detection and mitigation
- Evaluating and improving dataset quality
- The model aims to assist in creating datasets that are more balanced and representative, leading to better-performing machine learning models that are less biased. ## Training data
The GANdhi model was trained on a diverse set of datasets, which were preprocessed and curated to cover a broad range of domains and subjects. The exact datasets used in the training process are proprietary information. ## Model performance
GANdhi demonstrated effective performance in identifying biases and assessing fairness across various dataset distributions:

- Fairness scoring accuracy: 91.4% mean on ImageNet 1k
- Bias detection rate: 93.61% mean on COCO validation set ## Limitations
The GANdhi model has the following limitations:

- Sensitivity to noise: The model may be sensitive to noise present in the dataset, which could impact its fairness assessment performance.
- Incomplete domain coverage: While the model has been trained on diverse datasets, it may not cover all possible domains and contexts, leading to potential discrepancies in its performance.
TAGS
#license-bigscience-openrail-m #region-us 
# Model Card for GANdhi## Model description

GANdhi is a Generative Adversarial Network (GAN) based model designed to score the fairness of a dataset distribution. The model helps assess biases in datasets and provides valuable feedback to researchers and developers, ensuring a more equitable representation of data across various domains.

The page serves as a portflio place for the project, as the training data and model weight involves classfied information.## Intended use
GANdhi is intended for:

- Fairness assessment in dataset distribution
- Bias detection and mitigation
- Evaluating and improving dataset quality
- The model aims to assist in creating datasets that are more balanced and representative, leading to better-performing machine learning models that are less biased.## Training data
The GANdhi model was trained on a diverse set of datasets, which were preprocessed and curated to cover a broad range of domains and subjects. The exact datasets used in the training process are proprietary information.## Model performance
GANdhi demonstrated effective performance in identifying biases and assessing fairness across various dataset distributions:

- Fairness scoring accuracy: 91.4% mean on ImageNet 1k
- Bias detection rate: 93.61% mean on COCO validation set## Limitations
The GANdhi model has the following limitations:

- Sensitivity to noise: The model may be sensitive to noise present in the dataset, which could impact its fairness assessment performance.
- Incomplete domain coverage: While the model has been trained on diverse datasets, it may not cover all possible domains and contexts, leading to potential discrepancies in its performance.