I’ve always been fascinated by how machines can learn to see and describe the world around them. Recently, while working on a project that required generating descriptions for thousands of product images, I realized the power of combining computer vision with natural language processing. This experience inspired me to share how you can build your own image captioning system using PyTorch. Let’s explore how to create something that not only recognizes objects in images but also describes them in natural language.

Multi-modal AI systems bridge different types of data, and image captioning is a perfect example. Why do you think it’s challenging for a model to generate accurate and coherent captions? The key lies in designing an architecture that can process visual information and convert it into meaningful text. I’ll show you how to combine a CNN for image understanding with a Transformer for language generation.

Our system uses a CNN encoder to extract features from images and a Transformer decoder to generate captions. The CNN acts as the “eyes” of the model, identifying patterns and objects, while the Transformer serves as the “brain,” constructing sentences based on those visual cues. This combination allows the model to handle the complexity of both domains effectively.

Let’s start with the visual encoder. I prefer using a pre-trained ResNet model because it provides robust feature extraction without requiring extensive training from scratch. Here’s a simplified version of how we can implement it:

class VisualEncoder(nn.Module):
    def __init__(self, embed_dim=512):
        super(VisualEncoder, self).__init__()
        resnet = models.resnet101(pretrained=True)
        self.backbone = nn.Sequential(*list(resnet.children())[:-2])
        self.projection = nn.Linear(2048, embed_dim)
        self.layer_norm = nn.LayerNorm(embed_dim)
    
    def forward(self, images):
        features = self.backbone(images)
        features = features.view(features.size(0), -1, 2048)
        projected = self.projection(features)
        return self.layer_norm(projected)

This code takes an image and converts it into a sequence of feature vectors. Each vector represents a part of the image, which the Transformer can then use to generate words. How do you think we ensure these features align with the text generation process?

Now, for the language part, we use a Transformer decoder. It’s excellent for handling sequential data and can attend to different parts of the image when generating each word. Here’s a basic setup:

class CaptionDecoder(nn.Module):
    def __init__(self, vocab_size, embed_dim=512, num_layers=6):
        super(CaptionDecoder, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        decoder_layer = nn.TransformerDecoderLayer(d_model=embed_dim, nhead=8)
        self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)
        self.output = nn.Linear(embed_dim, vocab_size)
    
    def forward(self, text, visual_features):
        text_embed = self.embedding(text)
        output = self.decoder(text_embed, visual_features)
        return self.output(output)

During training, we use teacher forcing, where we feed the correct previous words to the decoder to stabilize learning. But what happens during inference when we don’t have the ground truth? We switch to autoregressive generation, predicting one word at a time based on the previous outputs.

Training such a model requires careful handling of data. We need to preprocess images and captions, often using datasets like COCO. I typically resize images to 224x224 and normalize them, while tokenizing captions into integer sequences. The loss function is usually cross-entropy, calculated between the predicted words and the actual captions.

One common challenge is overfitting, especially with limited data. I’ve found that using dropout and early stopping helps. Also, fine-tuning the CNN encoder gradually, rather than all at once, can lead to better performance. Have you encountered issues with model generalization in your projects?

For inference, we use beam search to generate high-quality captions. Instead of greedily picking the most likely word at each step, beam search keeps multiple possibilities and selects the best overall sequence. This often results in more fluent and accurate descriptions.

Evaluating the model involves metrics like BLEU score, which compares generated captions to reference ones. However, BLEU has limitations—it doesn’t always capture semantic accuracy. In practice, I combine it with human evaluation for a better assessment.

Building this system has taught me the importance of balancing visual and linguistic components. It’s not just about recognizing objects; it’s about understanding context and relationships. For instance, how would you describe a scene where a dog is chasing a ball? The model needs to infer action and intent from static pixels.

I encourage you to experiment with different architectures, such as using Vision Transformers for encoding or incorporating attention mechanisms in the CNN. The field is evolving rapidly, and there’s always room for innovation.

If you enjoyed this walkthrough and found it helpful, please like and share this article. I’d love to hear about your experiences or any questions you have in the comments below—let’s learn together!