Neural Storyteller: Image Captioning with Seq2Seq (ResNet50 + LSTM)

2026-02-14 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

A "Neural Storyteller" deep learning system has been developed to generate natural language captions from images using a Seq2Seq architecture. This multimodal AI model integrates a pre-trained ResNet50 CNN for feature extraction, removing its classification layer to produce a 2048-dimensional feature vector, which is then cached to reduce training time. An LSTM-based decoder, initialized by the encoder's output, generates captions. The system was trained on the Flickr30k dataset using PyTorch on Kaggle with dual GPU T4s, employing CrossEntropyLoss, Adam optimizer, early stopping, and teacher forcing. It implements both Greedy Search and Beam Search (k=3) for inference, with Beam Search yielding superior BLEU scores and more coherent captions. The model's performance was evaluated using BLEU-4, Precision, Recall, and F1-score, and it has been deployed as an interactive Streamlit application.

Key takeaway

For Machine Learning Engineers building image captioning systems, consider adopting a ResNet50 + LSTM Seq2Seq architecture with cached CNN features to optimize training. You should implement Beam Search for decoding to enhance caption quality and ensure comprehensive evaluation using metrics beyond just BLEU, such as Precision, Recall, and F1-score, to fully assess model performance.

Key insights

Bridging vision and language, multimodal AI systems can generate descriptive captions from raw image pixels.

Principles

• Caching CNN features accelerates training.
• Beam Search improves caption quality.
• Multiple metrics are needed for evaluation.

Method

A Seq2Seq model uses a pre-trained CNN (ResNet50) for feature extraction and an LSTM for caption generation, with Beam Search for decoding and evaluation via BLEU-4, Precision, Recall, and F1-score.

In practice

• Use ResNet50 for image feature extraction.
• Implement Beam Search for better captions.
• Cache features to speed up training.

Topics

• Image Captioning
• Seq2Seq Models
• ResNet50
• LSTM
• Beam Search

Code references

• Faizanyousaf140/neural-storyteller-image-captioning-seq2seq

Best for: Machine Learning Engineer, Deep Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Naturallanguageprocessing on Medium.