cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that can be adapted for custom datasets, making it useful for experimenting with retrieval systems in computer vision. By leveraging CNN architectures, the project showcases how learned embeddings can capture semantic similarity across varied images. This resource serves as both an educational reference and a foundation for further exploration in image retrieval research.
Features
- CNN-based feature extraction for image retrieval tasks
- Training and evaluation scripts for deep learning models
- Demonstrates retrieval using learned embeddings instead of handcrafted features
- Adaptable for custom datasets in computer vision experiments
- Provides examples of similarity-based image search
- Research-oriented implementation for academic exploration
Project Samples
