Overview This project aims to generate captions for images using the Flickr8K dataset, a deep learning model VGG16 for feature extraction, and an LSTM (Long Short-Term Memory) network for generating captions.
Dataset The dataset used for this project is the Flickr8K dataset, which consists of 8,000 images, each paired with five different captions. It's commonly used for image captioning tasks and provides a diverse set of images with human-generated descriptions.
Model Architecture VGG16: This pre-trained convolutional neural network (CNN) is used to extract features from the images. By passing the images through VGG16, we obtain a rich representation of the visual content. LSTM: A type of recurrent neural network (RNN), specifically designed to model sequential data. In this project, the LSTM network is responsible for generating captions based on the features extracted by VGG16. Usage Dataset Preparation: Download the Flickr8K dataset and preprocess it according to your needs. Ensure that the images and their corresponding captions are properly aligned. Feature Extraction: Use VGG16 to extract features from the images in the dataset. These features will serve as inputs to the LSTM network. Save the extracted features for future use to avoid recomputation. Model Training: Train the LSTM network using the extracted image features and their corresponding captions. Fine-tuning might be necessary depending on your specific requirements and the performance of the model. Caption Generation: Once the model is trained, you can generate captions for new images by first extracting their features using VGG16 and then feeding these features into the LSTM network to generate captions. Dependencies Ensure you have the following dependencies installed:
Python 3.x TensorFlow 2.15.1