The multimodal dataset features a diverse array of images accompanied by Gujarati language captions for each image. It encompasses image data, text captions, and comprehensive metadata.
Category
Visual Caption Dataset
Total Volume
25,000+ Captions
Last updated
Aug 2024
Number of participants
100+
Welcome to the Gujarati 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.
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.
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.
Each image-caption pair is accompanied by comprehensive metadata to facilitate informed decision-making in model development:
The Image Captioning Dataset serves various applications across different domains:
We understand the importance of evolving datasets to meet diverse research needs. Therefore, our dataset is regularly updated with new images and captions captured in various real-world conditions.
This Gujarati Language Image Captioning Dataset, created by FutureBeeAI, is available for commercial use.
Image Caption Models
Content Understanding
Multi Modal Learning
Dataset Sample(s)
Contact us to get the samples immediately for this dataset.
Dataset Type
Image Captioning Dataset
Language
Gujarati
Media
Multi-Modal Image & Text
Image Volume
5000+
Caption Volume
25000+ Captions
Image Format
JPEG, PNG, HEIC
Dataset Format
JSON, XLXS, TXT
Domain
Multiple
Annotation
Upon Request
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