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Siamese and triplet learning is a PyTorch implementation of Siamese and triplet neural network architectures designed for learning embedding representations in machine learning tasks. These types of networks learn to map images into a compact feature space where the distance between vectors reflects the similarity between inputs. Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train these models using contrastive loss and triplet loss functions, which encourage embeddings of similar samples to be close while pushing dissimilar samples farther apart. It includes data loaders, training scripts, neural network architectures, and evaluation metrics that allow researchers to experiment with different embedding learning strategies. The project also implements online pair and triplet mining techniques to efficiently generate training examples during model training.
Features
- PyTorch implementation of Siamese and triplet neural network architectures
- Embedding learning framework for similarity-based machine learning tasks
- Contrastive loss and triplet loss implementations for training models
- Dataset utilities that generate positive and negative pairs or triplets
- Online pair and triplet mining strategies for efficient training
- Example experiments using datasets such as MNIST and Fashion-MNIST
Project Samples
