
# GENERATED USA Passports Dataset

**Data generation** in machine learning involves creating or manipulating data to train and evaluate machine learning models. The purpose of data generation is to provide diverse and representative examples that cover a wide range of scenarios, ensuring the model's robustness and generalization.

Data augmentation techniques involve applying various transformations to existing data samples to create new ones. These transformations include: *random rotations, translations, scaling, flips, and more*. Augmentation helps in increasing the dataset size, introducing natural variations, and improving model performance by making it more invariant to specific transformations.

The dataset contains **GENERATED** USA passports, which are replicas of official passports but with randomly generated details, such as name, date of birth etc. The primary intention of generating these fake passports is to demonstrate the structure and content of a typical passport document and to train the neural network to identify this type of document.

Generated passports can assist in conducting research without accessing or compromising real user data that is often sensitive and subject to privacy regulations. Synthetic data generation allows researchers to develop and refine models using simulated passport data without risking privacy leaks.

### The dataset is solely for informational or educational purposes and should not be used for any fraudulent or deceptive activities.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2F30c6650541e63733f9ea0fcdc3bfc2cb%2FMacBook%20Air%20-%201%20(2).png?generation=1688719414649908&alt=media)

# Get the dataset

### This is just an example of the data

Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=generated-usa-passeports-dataset) to discuss your requirements, learn about the price and buy the dataset.

# Content

### Folders
- **original**: includes original generated images of USA passports
- **augmentation**: contains subfolders, corresponding to the original photos and including 3 black and white generated passport scans with different photo editing.

The augmentated photos are presented with random rotations, noise and brightness. Augmentation varies depending on the amount of noise and blur in the passport images, from slight (**us_pass_augmentated_1**) to significant (**us_pass_augmentated_3**).

### File with the extension .csv

includes the following information for each media file:

- **original**: link to access the image of the generated passport,
- **us_pass_augmentated_1**: link to the first augmentated image, 
- **us_pass_augmentated_2**: link to the second augmentated image, 
- **us_pass_augmentated_3**: link to the third augmentated image

# USA Passeport Photos might be generated in accordance with your requirements.

## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=generated-usa-passeports-dataset) provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**

TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
task_categories:image-to-image language:en license:cc-by-nc-nd-4.0 region:us
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-to-image"], "dataset_info": {"features": [{"name": "original", "dtype": "image"}, {"name": "us_pass_augmentated_1", "dtype": "image"}, {"name": "us_pass_augmentated_2", "dtype": "image"}, {"name": "us_pass_augmentated_3", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 224948826, "num_examples": 23}], "download_size": 142865341, "dataset_size": 224948826}}
TAGS
#task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #region-us 


# GENERATED USA Passports Dataset

Data generation in machine learning involves creating or manipulating data to train and evaluate machine learning models. The purpose of data generation is to provide diverse and representative examples that cover a wide range of scenarios, ensuring the model's robustness and generalization.

Data augmentation techniques involve applying various transformations to existing data samples to create new ones. These transformations include: *random rotations, translations, scaling, flips, and more*. Augmentation helps in increasing the dataset size, introducing natural variations, and improving model performance by making it more invariant to specific transformations.

The dataset contains GENERATED USA passports, which are replicas of official passports but with randomly generated details, such as name, date of birth etc. The primary intention of generating these fake passports is to demonstrate the structure and content of a typical passport document and to train the neural network to identify this type of document.

Generated passports can assist in conducting research without accessing or compromising real user data that is often sensitive and subject to privacy regulations. Synthetic data generation allows researchers to develop and refine models using simulated passport data without risking privacy leaks.

### The dataset is solely for informational or educational purposes and should not be used for any fraudulent or deceptive activities.

![](URL

# Get the dataset

### This is just an example of the data

Leave a request on URL to discuss your requirements, learn about the price and buy the dataset.

# Content

### Folders
- original: includes original generated images of USA passports
- augmentation: contains subfolders, corresponding to the original photos and including 3 black and white generated passport scans with different photo editing.

The augmentated photos are presented with random rotations, noise and brightness. Augmentation varies depending on the amount of noise and blur in the passport images, from slight (us_pass_augmentated_1) to significant (us_pass_augmentated_3).

### File with the extension .csv

includes the following information for each media file:

- original: link to access the image of the generated passport,
- us_pass_augmentated_1: link to the first augmentated image, 
- us_pass_augmentated_2: link to the second augmentated image, 
- us_pass_augmentated_3: link to the third augmentated image

# USA Passeport Photos might be generated in accordance with your requirements.

## TrainingData provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: URL

TrainingData's GitHub: URL
# GENERATED USA Passports Dataset

Data generation in machine learning involves creating or manipulating data to train and evaluate machine learning models. The purpose of data generation is to provide diverse and representative examples that cover a wide range of scenarios, ensuring the model's robustness and generalization.

Data augmentation techniques involve applying various transformations to existing data samples to create new ones. These transformations include: *random rotations, translations, scaling, flips, and more*. Augmentation helps in increasing the dataset size, introducing natural variations, and improving model performance by making it more invariant to specific transformations.

The dataset contains GENERATED USA passports, which are replicas of official passports but with randomly generated details, such as name, date of birth etc. The primary intention of generating these fake passports is to demonstrate the structure and content of a typical passport document and to train the neural network to identify this type of document.

Generated passports can assist in conducting research without accessing or compromising real user data that is often sensitive and subject to privacy regulations. Synthetic data generation allows researchers to develop and refine models using simulated passport data without risking privacy leaks. ### The dataset is solely for informational or educational purposes and should not be used for any fraudulent or deceptive activities.

![](URL # Get the dataset ### This is just an example of the data

Leave a request on URL to discuss your requirements, learn about the price and buy the dataset. # Content ### Folders
- original: includes original generated images of USA passports
- augmentation: contains subfolders, corresponding to the original photos and including 3 black and white generated passport scans with different photo editing.

The augmentated photos are presented with random rotations, noise and brightness. Augmentation varies depending on the amount of noise and blur in the passport images, from slight (us_pass_augmentated_1) to significant (us_pass_augmentated_3). ### File with the extension .csv

includes the following information for each media file:

- original: link to access the image of the generated passport,
- us_pass_augmentated_1: link to the first augmentated image, 
- us_pass_augmentated_2: link to the second augmentated image, 
- us_pass_augmentated_3: link to the third augmentated image # USA Passeport Photos might be generated in accordance with your requirements. ## TrainingData provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: URL

TrainingData's GitHub: URL
TAGS
#task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #region-us 
 # GENERATED USA Passports Dataset

Data generation in machine learning involves creating or manipulating data to train and evaluate machine learning models. The purpose of data generation is to provide diverse and representative examples that cover a wide range of scenarios, ensuring the model's robustness and generalization.

Data augmentation techniques involve applying various transformations to existing data samples to create new ones. These transformations include: *random rotations, translations, scaling, flips, and more*. Augmentation helps in increasing the dataset size, introducing natural variations, and improving model performance by making it more invariant to specific transformations.

The dataset contains GENERATED USA passports, which are replicas of official passports but with randomly generated details, such as name, date of birth etc. The primary intention of generating these fake passports is to demonstrate the structure and content of a typical passport document and to train the neural network to identify this type of document.

Generated passports can assist in conducting research without accessing or compromising real user data that is often sensitive and subject to privacy regulations. Synthetic data generation allows researchers to develop and refine models using simulated passport data without risking privacy leaks. ### The dataset is solely for informational or educational purposes and should not be used for any fraudulent or deceptive activities.

![](URL # Get the dataset ### This is just an example of the data

Leave a request on URL to discuss your requirements, learn about the price and buy the dataset. # Content ### Folders
- original: includes original generated images of USA passports
- augmentation: contains subfolders, corresponding to the original photos and including 3 black and white generated passport scans with different photo editing.

The augmentated photos are presented with random rotations, noise and brightness. Augmentation varies depending on the amount of noise and blur in the passport images, from slight (us_pass_augmentated_1) to significant (us_pass_augmentated_3). ### File with the extension .csv

includes the following information for each media file:

- original: link to access the image of the generated passport,
- us_pass_augmentated_1: link to the first augmentated image, 
- us_pass_augmentated_2: link to the second augmentated image, 
- us_pass_augmentated_3: link to the third augmentated image # USA Passeport Photos might be generated in accordance with your requirements. ## TrainingData provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: URL

TrainingData's GitHub: URL
passage: TAGS
#task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #region-us 
# GENERATED USA Passports Dataset

Data generation in machine learning involves creating or manipulating data to train and evaluate machine learning models. The purpose of data generation is to provide diverse and representative examples that cover a wide range of scenarios, ensuring the model's robustness and generalization.

Data augmentation techniques involve applying various transformations to existing data samples to create new ones. These transformations include: *random rotations, translations, scaling, flips, and more*. Augmentation helps in increasing the dataset size, introducing natural variations, and improving model performance by making it more invariant to specific transformations.

The dataset contains GENERATED USA passports, which are replicas of official passports but with randomly generated details, such as name, date of birth etc. The primary intention of generating these fake passports is to demonstrate the structure and content of a typical passport document and to train the neural network to identify this type of document.

Generated passports can assist in conducting research without accessing or compromising real user data that is often sensitive and subject to privacy regulations. Synthetic data generation allows researchers to develop and refine models using simulated passport data without risking privacy leaks.### The dataset is solely for informational or educational purposes and should not be used for any fraudulent or deceptive activities.

![](URL# Get the dataset### This is just an example of the data

Leave a request on URL to discuss your requirements, learn about the price and buy the dataset.# Content### Folders
- original: includes original generated images of USA passports
- augmentation: contains subfolders, corresponding to the original photos and including 3 black and white generated passport scans with different photo editing.

The augmentated photos are presented with random rotations, noise and brightness. Augmentation varies depending on the amount of noise and blur in the passport images, from slight (us_pass_augmentated_1) to significant (us_pass_augmentated_3).