Image Captioning Using ML Tools

Description

In this project, we employ CNN to determine the image's caption. Large datasets and powerful computers are helpful in the development of models that can create captions for images as deep learning techniques advance. In this Python-based project, we will use deep learning methods like CNN and RNN to put this into practice. To understand the context of a picture and deliver it in English, an image caption generator uses computer vision and natural language processing techniques. In this comprehensive work, we strictly adhere to some of the fundamental ideas and methods used in image captioning. For this project, we talk about the Keras library, numpy, and Jupyter notebooks. Additionally, we talk about how CNN and the flickr_dataset are utilized to classify images.

Languages and Utilities Used

• Python
• Jupyter(Colab)

Output

We have reviewed deep learning-based picture captioning methods in this project, along with a taxonomy, assessment criteria, datasets, and possible future research lines. Even though a lot of progress has been made, a reliable approach for creating high-quality captions for almost all photographs is still a way off. With the creation of cutting-edge deep learning network topologies, the topic of automatic picture captioning will continue to be a research hotspot.

Program walk-through:

Feature Extraction in Image:

Architecture of the Project:

Results:

[start] parents are pushing little children in red car carts [end]: