Generating Image, Video, and Audio Embeddings

Generating image embeddings

The dataset contains images of different pen types, sofas, cups, and glass.

Embedding model: Pretrained CNN (ResNet-18)

For image embeddings, the code utilizes a pretrained ResNet-18 model. ResNet (Residual Network) is a deep convolutional neural network architecture known for its effectiveness in image classification tasks. ResNet-18 consists of 18 layers and has shown strong performance on various image recognition benchmarks. We obtain a feature representation or embedding of the input image by removing the final fully connected layer. This embedding captures high-level features of the image, allowing us to perform tasks like similarity comparison and image retrieval.

We begin by importing necessary libraries. os is imported for interacting with the file system, torch for deep learning functionalities, torchvision.transforms for image transformations, torchvision.models for pretrained models, PIL for image processing, and cosine_similarity from sklearn.metrics.pairwise for computing cosine similarity between vectors.