christiansada / image-captioning-using-deep-learning

This project aims to generate descriptive captions for images using deep learning techniques. It combines computer vision and natural language processing to create meaningful descriptions of the content within images. The project utilizes the Flickr8K dataset, which consists of images and corresponding captions.

Image Captioning using Deep Learning

This project aims to generate descriptive captions for images using deep learning techniques. It combines computer vision and natural language processing to create meaningful descriptions of the content within images. The project utilizes the Flickr8K dataset, which consists of images and corresponding captions.

1. Introduction

Image captioning involves teaching a model to understand the content of an image and generate a textual description that accurately represents it. This project uses a combination of Convolutional Neural Networks (CNNs) for image feature extraction and Recurrent Neural Networks (RNNs) for sequence generation.

2. Libraries and Modules

The project utilizes several Python libraries and modules, including:

• NumPy
• Pandas
• Matplotlib
• NLTK
• TensorFlow and Keras

3. Exploratory Data Analysis

The dataset is explored to understand its structure and contents. This includes visualizing sample images along with their captions.

4. Text Processing and Analysis

Text data preprocessing techniques are applied to clean and prepare the captions for model training. This involves tasks such as removing punctuation, converting text to lowercase, and creating a vocabulary.

5. Train-Validation-Test Split

The dataset is split into training, validation, and test sets for model evaluation. This ensures that the model's performance is assessed on unseen data.

6. Feature Extraction

Image features are extracted using a pre-trained InceptionV3 CNN model. These features serve as input to the RNN model for generating captions.

7. Data Preparation

The textual captions are tokenized and converted into sequences for training the RNN model. Padding is applied to ensure uniform sequence lengths.

8. RNN Model

A sequential model architecture is defined using Keras, consisting of an embedding layer, LSTM layer, and dense layers. The model is trained on the training data and evaluated on the validation set.

9. Saving Files and Models

Trained models, tokenizer, and other necessary files are saved for future use and deployment.

10. Prediction & Performance Analysis

The trained model is used to generate captions for test images, and performance metrics such as BLEU score are calculated to evaluate the quality of generated captions. Beam search is also implemented to improve caption generation.

Results

• The model achieves competitive performance in generating descriptive captions for images.
• Performance metrics such as BLEU score indicate the quality of generated captions.
• Sample images with true captions and generated captions are visualized to demonstrate the model's effectiveness.

Conclusion

This project demonstrates the effectiveness of deep learning techniques in image captioning tasks. It serves as a foundation for further research and application in areas such as assistive technology, content creation, and image understanding.