Project Title : Image Caption Generator

Objective:

To build a system that automatically generates relevant captions for images by combining machine learning techniques from both computer vision and natural language processing (NLP).

Tools & Technologies:

Programming Language: Python

Libraries: TensorFlow / Keras, NumPy, Matplotlib, NLTK

Models: CNN (e.g., InceptionV3, VGG16) for image feature extraction,
LSTM for sequence generation

Dataset: Flickr8k, Flickr30k, or MS COCO

 How It Works (Machine Learning Approach):

Preprocessing Images:

Use a pre-trained CNN (like InceptionV3) to extract features (bottleneck features) from images.

These features are treated as input vectors representing the image.

Processing Captions:

Clean and tokenize text data.

Convert captions into sequences of integers using a vocabulary dictionary.

Model Architecture:

Image features + Text input are merged and passed into an LSTM network.

The LSTM predicts the next word in the sequence until a full caption is generated.

Training:

Train the model on image-caption pairs using a supervised learning approach.

Use cross-entropy loss and Adam optimizer to adjust weights.

Prediction:

For a new image, extract features → feed to the model → generate caption word-by-word.

 Evaluation Metrics:

BLEU Score (Bilingual Evaluation Understudy)

METEOR Score

Human evaluation (optional)

What Students Learn:

Integration of computer vision and NLP

Use of pre-trained models (transfer learning)

Data preprocessing in ML pipelines

Sequence modeling using RNNs/LSTMs

Practical implementation of end-to-end ML systems

Possible Extensions:

Use Transformer-based models (e.g., BERT or ViT + GPT)

Add attention mechanism to improve results

Deploy as a web or mobile app