Create Captivating Captions with Image Caption Generator

Table of Contents

1. Introduction
2. Deep Learning and Computer Vision
3. Image Caption Generator
4. Architecture of Image Caption Generator
5. Data Set and Pre-processing Steps
6. Training the Model
7. Generating Captions for Images
8. Model Accuracy and Improvements
9. Conclusion

Deep Learning Project - Image Caption Generator

Are You interested in learning about the latest trend in deep learning and computer vision? In this article, we will discuss the emerging field of image caption generator in Detail.

Deep Learning and Computer Vision

Deep learning is a subset of machine learning that teaches computers to recognize Patterns and make decisions Based on that knowledge. Visual recognition is one area in which deep learning has made significant progress. Computer vision, another important field, enables computers to perceive the world through digital images or videos.

Image Caption Generator

Image caption generator is a deep learning model that involves computer vision and natural language processing concepts. In this project, We Are generating captions for a given image using convolutional neural networks (CNN) and long short-term memory (LSTM) architecture.

Architecture of Image Caption Generator

The architecture of image caption generator involves taking the image, passing it through the CNN for image processing, and then using LSTM for generating captions based on the processed image output. After combining the output of CNN and LSTM, we will generate captions for the respective images.

Data Set and Pre-processing Steps

We have used the Flickr 8k data set, which includes 8,000 images and their respective captions. We loaded the path for images and captions and pre-processed the captions by storing them in the token dictionary and generating respective tokens.

Training the Model

We partitioned the data set, where 6,000 images were for training, and the remaining were for testing. Pretend ImageNet model was used for transfer learning, and model weights created were used for training.

Generating Captions for Images

After the pre-processing steps and training, we generated captions for images by processing the images, creating neural network layers for both images and captions, and concatenating the two models.

Model Accuracy and Improvements

Based on the generated captions, our model accuracy for image caption generation stands at approximately 40%. However, by increasing the epochs and adding more layers, the accuracy can be improved, leading to more accurate caption predictions.

Conclusion

Deep learning and computer vision are rapidly developing fields, and image caption generator is an exciting example of the progress made in these areas. With pre-trained models and transfer learning, we can develop image caption generators with reasonable accuracy.