Image Caption Generator with VGG16 and LSTM
This repository implements an image caption generator model using VGG16 for feature extraction and LSTM for caption generation. It leverages the power of deep learning for automatic image description.
Features:
Pre-trained VGG16: Utilizes the pre-trained VGG16 model to efficiently extract high-level features from images. LSTM Architecture: Employs Long Short-Term Memory (LSTM) networks to capture the sequential nature of language and generate coherent captions. Trainable on custom datasets: Can be trained on your own image-caption datasets for specific needs. Requirements:
Python 3.x TensorFlow/Keras Usage:
Clone the repository: git clone https://github.com/sai-srinivasan-v/congenial-tribble.git Install dependencies: pip install -r requirements.txt Prepare your dataset: Preprocess your image-caption data and organize it into appropriate formats. Train the model: Run the provided script with your dataset paths and training parameters. Generate captions: Use the trained model to generate captions for new images. Further Exploration:
Experiment with different hyperparameters for model optimization. Explore more advanced architectures like attention mechanisms for improved caption quality. Integrate the model into a web application for user-friendly image captioning. Note: This repository provides a basic implementation of an image caption generator. It can be further enhanced and customized for specific needs and research purposes.