
# Arabic Translated COCO Validation Dataset

---

## Overview

Welcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language.

## Contents

1. **coco_url:** This column includes images URL which makes a subset of the COCO validation images.

2. **arabic_caption:** Arabic translations of the original COCO annotations, providing detailed information about image captions.

## Usage

- **Research and Development:** Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.

- **Benchmarking:** Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research.

## Dataset Translation and Bias

This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses.




task_categories:image-to-text size_categories:100K<n<1M language:ar region:us
{"language": ["ar"], "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"], "pretty_name": "Arabic COCO 2014 Validation"}
TAGS
#task_categories-image-to-text #size_categories-100K<n<1M #language-Arabic #region-us 


# Arabic Translated COCO Validation Dataset

---

## Overview

Welcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language.

## Contents

1. coco_url: This column includes images URL which makes a subset of the COCO validation images.

2. arabic_caption: Arabic translations of the original COCO annotations, providing detailed information about image captions.

## Usage

- Research and Development: Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.

- Benchmarking: Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research.

## Dataset Translation and Bias

This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses.




# Arabic Translated COCO Validation Dataset

--- ## Overview

Welcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language. ## Contents

1. coco_url: This column includes images URL which makes a subset of the COCO validation images.

2. arabic_caption: Arabic translations of the original COCO annotations, providing detailed information about image captions. ## Usage

- Research and Development: Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.

- Benchmarking: Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research. ## Dataset Translation and Bias

This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses.
TAGS
#task_categories-image-to-text #size_categories-100K<n<1M #language-Arabic #region-us 
 # Arabic Translated COCO Validation Dataset

--- ## Overview

Welcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language. ## Contents

1. coco_url: This column includes images URL which makes a subset of the COCO validation images.

2. arabic_caption: Arabic translations of the original COCO annotations, providing detailed information about image captions. ## Usage

- Research and Development: Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.

- Benchmarking: Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research. ## Dataset Translation and Bias

This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses.
passage: TAGS
#task_categories-image-to-text #size_categories-100K<n<1M #language-Arabic #region-us 
# Arabic Translated COCO Validation Dataset

---## Overview

Welcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language.## Contents

1. coco_url: This column includes images URL which makes a subset of the COCO validation images.

2. arabic_caption: Arabic translations of the original COCO annotations, providing detailed information about image captions.## Usage

- Research and Development: Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.

- Benchmarking: Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research.## Dataset Translation and Bias

This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses.