Image Captioning CNN-LSTM: An End-to-End Image Description Generation Project Based on PyTorch

This project is a complete implementation of image description generation, using ResNet-50 as the CNN encoder to extract image features and LSTM as the decoder to generate natural language descriptions. The project includes full vocabulary construction, data preprocessing, training pipeline (supporting BLEU evaluation), inference functionality, as well as metric recording, model checkpoint saving, and visualization output. It is an excellent introductory project for understanding image description generation technology.

Image Captioning is an interdisciplinary field of computer vision and natural language processing, aiming to enable computers to understand image content and generate descriptive text. It is applied in assisting visually impaired people, image retrieval, social media, medical imaging, autonomous driving, and other fields. Early methods relied on handcrafted features and templates with limited results; after 2015, deep learning approaches (encoder-decoder architecture, attention mechanism, Transformer, multimodal large models) completely transformed this field. This project adopts the classic CNN-LSTM architecture, which is traditional but suitable for beginners.

The project uses an encoder-decoder architecture: Input Image → ResNet-50 Encoder → Feature Vector → LSTM Decoder → Natural Language Description.

• CNN Encoder (ResNet-50)：Balances depth and efficiency, uses residual connections to solve gradient vanishing, leverages ImageNet pre-trained weights for strong feature extraction, converting images into 2048-dimensional feature vectors.
• LSTM Decoder：Uses memory capabilities to capture long-range dependencies, gate mechanisms to control information flow, combines the previous hidden state with image features/previous word embeddings at each time step, and outputs word probability distributions.
• Vocabulary Construction：Includes word segmentation, lowercase conversion, punctuation processing, special tokens (e.g., ), filters low-frequency words (replaces with ), and the vocabulary size is usually 5000-10000 words.

Data Preparation：Supports Flickr8k/30k, COCO Captions, and custom datasets. Loss Function：Cross-entropy loss, maximizing the log probability of each word in the target description. BLEU Evaluation：Integrates BLEU-1 to BLEU-4 metrics to measure n-gram matching, with BLEU-4 having the highest correlation with human judgment. Training Techniques：Learning rate scheduling, gradient clipping, Dropout, Early Stopping, checkpoint saving (supports resuming training from breakpoints).

Inference Methods：

• Greedy Decoding：Selects the word with the highest probability at each step, simple and fast but may generate repetitive content.
• Beam Search：Maintains k candidate sequences, produces higher-quality results but increases computational cost. Project Highlights：
• Complete Workflow：Automated pipeline from data preparation to deployment.
• Modular Design：Clear code structure (scripts like models/data/utils).
• Detailed Documentation：Instructions for environment configuration, dataset preparation, training/inference commands, etc.

Application Scenarios：

• Educational Use：Introduction to deep learning, multimodal learning, PyTorch practice, sequence generation tasks.
• Research Foundation：Can serve as a starting point for research on attention mechanism improvement, Transformer replacement, reinforcement learning optimization, etc.
• Practical Applications：Photo album annotation, content moderation assistance, e-commerce product descriptions, news image captioning. Comparison with Modern Methods：

  Feature	This Project (CNN-LSTM)	CLIP-based Models	Multimodal Large Models
  Architecture Complexity	⭐⭐ Simple	⭐⭐⭐ Medium	⭐⭐⭐⭐⭐ Complex
  Training Cost	⭐ Low	⭐⭐ Medium	⭐⭐⭐⭐⭐ Very High
  Inference Speed	⭐⭐⭐⭐⭐ Fast	⭐⭐⭐⭐ Fast	⭐⭐ Slow
  Generation Quality	⭐⭐⭐ Good	⭐⭐⭐⭐ Very Good	⭐⭐⭐⭐⭐ Excellent
  Interpretability	⭐⭐⭐⭐⭐ High	⭐⭐⭐ Medium	⭐⭐ Low
  Resource Requirement	⭐ Low	⭐⭐ Medium	⭐⭐⭐⭐⭐ Very High

Improvement Directions：

• Short-term：Attention visualization, data augmentation, label smoothing, learning rate warm-up.
• Mid-term：Replace with Transformer decoder, integrate pre-trained language models, multi-scale features, adversarial training.
• Long-term：CLIP integration, multimodal pre-training, controllable generation, multimodal output. Summary：This project is an excellent teaching and research foundation project, fully implementing the classic encoder-decoder architecture, concise and highly interpretable, laying a solid foundation for learning advanced vision-language models. It is suitable for developers, students, or researchers who are new to multimodal AI.