4 repository-uri
Methods for generating and normalizing feature embeddings for machine learning models.
Distinguishing note: Focuses on the mathematical computation and normalization of embeddings rather than general model training.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Embedding Computation. Refine with filters or upvote what's useful.
InsightFace is a comprehensive deep learning framework designed for face recognition, biometric identity verification, and feature extraction. It provides a specialized engine for one-to-one verification and one-to-many identification tasks, utilizing convolutional neural networks to transform raw image pixels into high-dimensional vector embeddings. The project includes a complete toolkit for detecting, aligning, and processing facial data to ensure consistent identity discrimination. Beyond core recognition, the platform distinguishes itself through an extensive model management and optimiz
InsightFace computes normalized embeddings using official preprocessing pipelines and determines optimal similarity thresholds on a validation split to ensure reproducible and accurate results.
PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti
Learns object detection and appearance embedding tasks simultaneously within a shared network.
PyTorch Metric Learning is an open-source library for training neural networks to produce similarity-preserving embedding spaces. It provides a modular framework where interchangeable loss functions, mining strategies, and evaluation tools can be composed to learn representations that map similar items to nearby points and dissimilar items to distant points in the embedding space. The library distinguishes itself through a highly configurable architecture that separates concerns across several interchangeable components. Users can assemble custom loss functions from pluggable distance metrics
Provides loss functions that compute penalties from embeddings and labels for metric learning.
TransformerLens is a library for mechanistic interpretability research designed to reverse engineer the learned algorithms within large language models. It provides a standardized framework for wrapping diverse transformer architectures, allowing researchers to extract, manipulate, and analyze internal activations and weights through a consistent interface. The project distinguishes itself through a comprehensive system of activation hooks that can capture, patch, and ablate internal tensors during the forward pass. It includes specialized utilities for decomposing fused projections, material
Computes and normalizes combined token, positional, and token-type embeddings for model inputs.