Basic Info

The COCO dataset (Common Objects in Context) is a large-scale dataset of images and annotations for object detection, segmentation, and captioning. It is one of the most popular datasets in computer vision research, and has been used to train and evaluate many state-of-the-art models.

The COCO dataset is a valuable resource for researchers working on object detection, segmentation, and captioning. It is a large, challenging dataset that provides a wide variety of images and annotations. The COCO dataset is also well-organized and easy to use.

To have more info about the dataset, you can visit its website

The Data Uploaded Consists of Images and their respective annotations to be used for Image captioning

2014 Data

The 2014 Data is used as Training and Validation data for the task of Image captioning

2017 Data

The 2017 Data is used as Testing data

Unlock detailed insights into road scenes with our Vehicle Image Captioning Dataset. Featuring over 1000 high-resolution images.

RSICD Image Caption Dataset

RSICD Image Caption Dataset

By Arto (From Huggingface) [source]

About this dataset

The train.csv file contains a list of image filenames, captions, and the actual images used for training the image captioning models. Similarly, the test.csv file includes a separate set of image filenames, captions, and images specifically designated for testing the accuracy and performance of the trained models.

Furthermore, the valid.csv file contains a unique collection of image filenames with their respective captions and images that serve as an independent validation set to evaluate the models' capabilities accurately.

Each entry in these CSV files includes both a filename string that indicates the name or identifier of an image file stored in another location or directory. Additionally,** each entry also provides a list (or multiple rows) o**f strings representing written descriptions or captions describing each respective image given its filename.

Considering these details about this dataset's structure, it can be immensely valuable to researchers, developers, and enthusiasts working on developing innovative computer vision algorithms such as automatic text generation based on visual content analysis. Whether it's training machine learning models to automatically generate relevant captions based on new unseen images or evaluating existing systems' performance against diverse criteria.

Stay updated with cutting-edge research trends by leveraging this comprehensive dataset containing not only captio**ns but also corresponding imag**es across different sets specifically designed to cater to varied purposes within computer vision tasks. »

How to use the dataset

Overview of the Dataset

The dataset consists of three primary files: train.csv, test.csv, and valid.csv. These files contain information about image filenames and their respective captions. Each file includes multiple captions for each image to support diverse training techniques.

Understanding the Files

• train.csv: This file contains filenames (filename column) and their corresponding captions (captions column) for training your image captioning model.
• test.csv: The test set is included in this file, which contains a similar structure as that of train.csv. The purpose of this file is to evaluate your trained models on unseen data.
• valid.csv: This validation set provides images with their respective filenames (filename) and captions (captions). It allows you to fine-tune your models based on performance during evaluation.

Getting Started

To begin utilizing this dataset effectively, follow these steps:

• Extract the zip file containing all relevant data files onto your local machine or cloud environment.
• Familiarize yourself with each CSV file's structure: train.csv, test.csv, and valid.csv. Understand how information like filename(s) (filename) corresponds with its respective caption(s) (captions).
• Depending on your specific use case or research goals, determine which portion(s) of the dataset you wish to work with (e.g., only train or train+validation).
• Load the dataset into your preferred programming environment or machine learning framework, ensuring you have the necessary dependencies installed.
• Preprocess the dataset as needed, such as resizing images to a specific dimension or encoding captions for model training purposes.
• Split the data into training, validation, and test sets according to your experimental design requirements.
• Use appropriate algorithms and techniques to train your image captioning models on the provided data.

Enhancing Model Performance

To optimize model performance using this dataset, consider these tips:

• Explore different architectures and pre-trained models specifically designed for image captioning tasks.
• Experiment with various natural language

Research Ideas

• Image Captioning: This dataset can be used to train and evaluate image captioning models. The captions can be used as target labels for training, and the images can be paired with the captions to generate descriptive captions for test images.
• Image Retrieval: The dataset can be used for image retrieval tasks where given a query caption, the model needs to retrieve the images that best match the description. This can be useful in applications such as content-based image search.
• Natural Language Processing: The dataset can also be used for natural language processing tasks such as text generation or machine translation. The captions in this dataset are descriptive ...

This dataset consists of 2000 images used from (PEER Hub Image-Net) and a caption file in text format.

The caption uses a very limited vocabulary set designed to get better results while also describing plenty of information with a length of 18 vocab.

Introduction

Welcome to the English Language Image Captioning Dataset! A collection of Images with associated text captions to facilitate the development of AI models capable of generating high-quality captions for images. This dataset is meticulously crafted to support research and innovation in computer vision and natural language processing.

Image Data

This dataset features over 5,000 high-resolution images sourced from diverse categories and scenes. Each image is meticulously selected to encompass a wide array of contexts, objects, and environments, ensuring comprehensive coverage for training robust image captioning models.

Caption Data

Each image in the dataset is paired with a high-quality descriptive caption. These captions are carefully crafted to provide detailed and contextually rich descriptions of the images, enhancing the dataset's utility for training sophisticated image captioning algorithms.

Metadata

Each image-caption pair is accompanied by comprehensive metadata to facilitate informed decision-making in model development:

Usage and Applications

The Image Captioning Dataset serves various applications across different domains:

Dataset Card for "kag100-image-captioning-dataset"

More Information needed

Dataset Card for "my-image-captioning-dataset"

More Information needed

Dataset

A small image captioning dataset that is perfect to get started in image captioning. I have also made a video on building an image captioning model in PyTorch where we use this dataset that you could check out: https://youtu.be/y2BaTt1fxJU

Dataset Card for "fkr30k-image-captioning-dataset"

More Information needed

Introduction

Welcome to the Polish Language Image Captioning Dataset! A collection of Images with associated text captions to facilitate the development of AI models capable of generating high-quality captions for images. This dataset is meticulously crafted to support research and innovation in computer vision and natural language processing.

Image Data

This dataset features over 5,000 high-resolution images sourced from diverse categories and scenes. Each image is meticulously selected to encompass a wide array of contexts, objects, and environments, ensuring comprehensive coverage for training robust image captioning models.

Caption Data

Each image in the dataset is paired with a high-quality descriptive caption. These captions are carefully crafted to provide detailed and contextually rich descriptions of the images, enhancing the dataset's utility for training sophisticated image captioning algorithms.

Metadata

Each image-caption pair is accompanied by comprehensive metadata to facilitate informed decision-making in model development:

Usage and Applications

The Image Captioning Dataset serves various applications across different domains:

Introduction

Welcome to the Dutch Language Image Captioning Dataset! A collection of Images with associated text captions to facilitate the development of AI models capable of generating high-quality captions for images. This dataset is meticulously crafted to support research and innovation in computer vision and natural language processing.

Image Data

This dataset features over 5,000 high-resolution images sourced from diverse categories and scenes. Each image is meticulously selected to encompass a wide array of contexts, objects, and environments, ensuring comprehensive coverage for training robust image captioning models.

Caption Data

Each image in the dataset is paired with a high-quality descriptive caption. These captions are carefully crafted to provide detailed and contextually rich descriptions of the images, enhancing the dataset's utility for training sophisticated image captioning algorithms.

Metadata

Each image-caption pair is accompanied by comprehensive metadata to facilitate informed decision-making in model development:

Usage and Applications

The Image Captioning Dataset serves various applications across different domains:

Remote Sensing Image Captioning Dataset (RSICD) and UCM-captions dataset for remote sensing image captioning

blip-image-captioning-base https://huggingface.co/Salesforce/blip-image-captioning-base blip-image-captioning-large https://huggingface.co/Salesforce/blip-image-captioning-large blip2-flan-t5-xl https://huggingface.co/Salesforce/blip2-flan-t5-xl blip2-opt-2.7b https://huggingface.co/Salesforce/blip2-opt-2.7b git-base https://huggingface.co/microsoft/git-base git-base-coco https://huggingface.co/microsoft/git-base-coco git-large-coco https://huggingface.co/microsoft/git-large-coco git-large-r https://huggingface.co/microsoft/git-large-r image-caption-generator https://huggingface.co/bipin/image-caption-generator image_caption https://huggingface.co/jaimin/image_caption vit-gpt2-coco-en https://huggingface.co/ydshieh/vit-gpt2-coco-en vit-gpt2-image-captioning https://huggingface.co/nlpconnect/vit-gpt2-image-captioning vit-swin-base-224-gpt2-image-captioning https://huggingface.co/Abdou/vit-swin-base-224-gpt2-image-captioning

The SwaFlickr8k dataset is an extension of the well-known Flickr8k dataset, specifically designed for image captioning tasks. It includes a collection of images and corresponding captions written in Swahili. With 8,091 unique images and 40,455 captions, this dataset provides a valuable resource for research and development in the field of image understanding and language processing, particularly in the context of Swahili language.

akibc123/my-image-captioning-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Introduction

Welcome to the Vietnamese Language Image Captioning Dataset! A collection of Images with associated text captions to facilitate the development of AI models capable of generating high-quality captions for images. This dataset is meticulously crafted to support research and innovation in computer vision and natural language processing.

Image Data

This dataset features over 5,000 high-resolution images sourced from diverse categories and scenes. Each image is meticulously selected to encompass a wide array of contexts, objects, and environments, ensuring comprehensive coverage for training robust image captioning models.

Caption Data

Each image in the dataset is paired with a high-quality descriptive caption. These captions are carefully crafted to provide detailed and contextually rich descriptions of the images, enhancing the dataset's utility for training sophisticated image captioning algorithms.

Metadata

Each image-caption pair is accompanied by comprehensive metadata to facilitate informed decision-making in model development:

Usage and Applications

The Image Captioning Dataset serves various applications across different domains:

kasvnmtp/vlm-image-captioning-dataset

  Dataset Description

This is a custom Vision Language Model (VLM) dataset for image captioning tasks. The dataset contains image-text pairs suitable for finetuning vision-language models.

  Dataset Statistics

Total Samples: 149,997 Train Samples: 74,998 Test Samples: 74,999 Features: image, text, sample_id

  Dataset Structure





  Data Fields

image: PIL Image object text: Caption/description text for the… See the full description on the dataset page: https://huggingface.co/datasets/kasvnmtp/vlm-image-captioning-dataset.

etc.

Web-Harvested Image and Caption Dataset

Web-Harvested Image and Caption Dataset

By conceptual_captions (From Huggingface) [source]

About this dataset

The Conceptual Captions dataset, hosted on Kaggle, is a comprehensive and expansive collection of web-harvested images and their corresponding captions. With a staggering total of approximately 3.3 million images, this dataset offers a rich resource for training and evaluating image captioning models.

Unlike other image caption datasets, the unique feature of Conceptual Captions lies in the diverse range of styles represented in its captions. These captions are sourced from the web, specifically extracted from the Alt-text HTML attribute associated with web images. This approach ensures that the dataset encompasses a broad variety of textual descriptions that accurately reflect real-world usage scenarios.

To guarantee the quality and reliability of these captions, an elaborate automatic pipeline has been developed for extracting, filtering, and transforming each image/caption pair. The goal behind this diligent curation process is to provide clean, informative, fluent, and learnable captions that effectively describe their corresponding images.

The dataset itself consists of two primary components: train.csv and validation.csv files. The train.csv file comprises an extensive collection of over 3.3 million web-harvested images along with their respective carefully curated captions. Each image is accompanied by its unique URL to allow easy retrieval during model training.

On the other hand, validation.csv contains approximately 100,000 image URLs paired with their corresponding informative captions. This subset serves as an invaluable resource for validating and evaluating model performance after training on the larger train.csv set.

Researchers and data scientists can leverage this remarkable Conceptual Captions dataset to develop state-of-the-art computer vision models focused on tasks such as image understanding, natural language processing (NLP), multimodal learning techniques combining visual features with textual context comprehension – among others.

By providing such an extensive array of high-quality images coupled with richly descriptive captions acquired from various sources across the internet landscape through a meticulous curation process - Conceptual Captions empowers professionals working in fields like artificial intelligence (AI), machine learning, computer vision, and natural language processing to explore new frontiers in visual understanding and textual comprehension

How to use the dataset

Title: How to Use the Conceptual Captions Dataset for Web-Harvested Image and Caption Analysis

Introduction: The Conceptual Captions dataset is an extensive collection of web-harvested images, each accompanied by a caption. This guide aims to help you understand and effectively utilize this dataset for various applications, such as image captioning, natural language processing, computer vision tasks, and more. Let's dive into the details!

Step 1: Acquiring the Dataset

Step 2: Exploring the Dataset Files After downloading the dataset files ('train.csv' and 'validation.csv'), you'll find that each file consists of multiple columns containing valuable information:

a) 'caption': This column holds captions associated with each image. It provides textual descriptions that can be used in various NLP tasks. b) 'image_url': This column contains URLs pointing to individual images in the dataset.

Step 3: Understanding Dataset Structure The Conceptual Captions dataset follows a tabular format where each row represents an image/caption pair. Combining knowledge from both train.csv and validation.csv files will give you access to a diverse range of approximately 3.4 million paired examples.

Step 4: Preprocessing Considerations Due to its web-harvested nature, it is recommended to perform certain preprocessing steps on this dataset before utilizing it for your specific task(s). Some considerations include:

a) Text Cleaning: Perform basic text cleaning techniques such as removing special characters or applying sentence tokenization. b) Filtering: Depending on your application, you may need to apply specific filters to remove captions that are irrelevant, inaccurate, or noisy. c) Language Preprocessing: Consider using techniques like lemmatization or stemming if it suits your task.

Step 5: Training and Evaluation Once you have preprocessed the dataset as per your requirements, it's time to train your models! The Conceptual Captions dataset can be used for a range of tasks such as image captioni...

Explore the Flickr8k Image Dataset, featuring 8,092 images with multiple descriptive captions. This dataset is widely used for image captioning, visual recognition, and AI research in computer vision and natural language processing.