Image Captioning with Deep Learning
Description:
This project aims to implement a deep learning model for generating captions for images. Leveraging convolutional neural networks (CNNs) for feature extraction and recurrent neural networks (RNNs) for sequence generation, the model learns to understand the content of images and produce descriptive captions.
Features:
Image Preprocessing: Utilizes pre-trained CNNs (e.g., VGG, ResNet) to extract image features. Caption Generation: Implements an RNN-based architecture (e.g., LSTM, GRU) to generate captions based on the extracted image features. Evaluation Metrics: Incorporates evaluation metrics like BLEU (Bilingual Evaluation Understudy) score for assessing caption quality. Training and Inference Modes: Provides scripts for training the model on custom datasets and generating captions for unseen images. Visualization: Includes visualization tools to understand the attention mechanism and improve model interpretability. Deployment: Offers options for deploying the model for real-time captioning or integrating it into existing applications.