Yes, you can use image embeddings from product photos to solve a variety of technical problems. Image embeddings are numerical representations of images generated by deep learning models, which capture visual features like shapes, colors, and textures in a compact vector format. These embeddings make it easier to compare, search, or classify images programmatically. For example, a model trained on product photos can generate embeddings that represent distinctions between shoes, shirts, or electronics, enabling tasks like visual search or recommendation systems without requiring manual feature engineering.
One practical application is building a product similarity search system. Suppose you have an e-commerce platform with thousands of product images. By generating embeddings for each photo using a pre-trained model like ResNet or CLIP, you can compute similarity scores between vectors to find visually similar items. For instance, a user viewing a red dress could be shown other dresses with similar patterns or silhouettes. Embeddings also enable clustering products into categories automatically. If you’re dealing with user-generated photos (e.g., marketplace listings), embeddings can help detect duplicate or fraudulent listings by identifying near-identical images, even if lighting or angles differ slightly.
To implement this, you’d first use a pre-trained convolutional neural network (CNN) or vision transformer to extract embeddings. Libraries like TensorFlow, PyTorch, or Hugging Face Transformers provide off-the-shelf models for this. For example, using PyTorch’s torchvision.models.resnet50, you can remove the final classification layer and extract the 2048-dimensional vector from the second-to-last layer. These vectors can be stored in a database optimized for vector search, such as FAISS, Annoy, or Elasticsearch’s dense vector type. To scale, you might batch-process images on a serverless platform like AWS Lambda or use a dedicated inference service. Keep in mind that model choice impacts performance: lightweight models like MobileNet trade accuracy for speed, while larger models like CLIP offer cross-modal capabilities (e.g., matching text queries to images). Testing different models and normalization techniques (e.g., L2 normalization) will help optimize results for your specific use case.