Category: Deep Learning · Computer Vision · Natural Language Processing
An end-to-end Image Captioning System that automatically generates natural language descriptions for images by combining CNN-based visual feature extraction with LSTM-based sequence modeling.
Image Captioning is a challenging task at the intersection of Computer Vision (CV) and Natural Language Processing (NLP), where the objective is to enable machines to understand visual content and describe it in human language.
This project implements an encoder–decoder deep learning architecture:
- Encoder: Extracts high-level visual representations from images using a CNN.
- Decoder: Generates captions sequentially, predicting one word at a time based on visual features and previous words.
The model is trained on paired image–caption data and evaluated using standard NLP metrics such as the BLEU score.
- Extract discriminative and meaningful visual features from images
- Generate grammatically correct and semantically relevant captions
- Train the model efficiently using a custom data generator
- Evaluate performance using standard NLP evaluation metrics
- Test caption generation on unseen and external images
- Pre-trained CNN for image feature extraction
- Fully connected layer to project extracted features into a compact embedding space
- Word embedding layer to represent textual input
- LSTM network for sequential caption generation
- Fully connected + Softmax layer for next-word prediction
- Teacher forcing for faster and more stable convergence
- Loss function: Categorical Cross-Entropy
- Optimizer: Adam
- Programming Language: Python 3.x
- Framework: TensorFlow / Keras
- Computer Vision: CNN-based feature extraction
- Natural Language Processing: Tokenization, word embeddings, LSTM
Libraries:
- NumPy, Pandas
- PIL, OpenCV
- Matplotlib
- NLTK (BLEU score evaluation)
- tqdm
Environment: Anaconda / Virtual Environment
The model performance is evaluated using:
- BLEU-1
- BLEU-2
A BLEU score above 0.4 is generally considered a good result for image captioning tasks. Higher scores can be achieved by increasing the number of training epochs or improving the model architecture (e.g., attention mechanisms).
image-captioning-ai/
├─ data/
│ ├─ Images/ # Image dataset
│ └─ captions/ # Caption annotations
│
├─ notebooks/
│ └─ image_captioning.ipynb # Training, evaluation, and visualization
│
├─ models/
│ └─ image_caption_model.keras
│
├─ README.md
├─ requirements.txt
└─ .gitignore
- Integrating an attention mechanism
- Using beam search instead of greedy decoding
- Fine-tuning the CNN encoder
- Training on larger and more diverse datasets