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This project is a self-supervised contrastive learning framework designed to train deep learning models to learn visual representations from images without using human-provided labels. It provides a system for developing pretrained visual representation models that can be adapted for downstream computer vision tasks. The framework includes tools for semi-supervised image classification, which combines large unlabeled datasets with small labeled sets to improve accuracy. It also features a linear probe evaluation tool to assess the quality of learned image features by training a simple linear
Lightly is a self-supervised learning framework and computer vision data curation tool designed to manage large image datasets and train models on unlabeled data. It functions as a PyTorch vision library and dataset management SDK, providing tools to convert raw images into high-dimensional vectors for similarity search, visualization, and feature extraction. The project implements a variety of self-supervised architectures, including MoCo, SimCLR, VICReg, Barlow Twins, and masked image modeling. It distinguishes itself by combining these learning frameworks with active learning capabilities,
This project is a PyTorch vision transformer framework designed for self-supervised learning. It implements a model that trains visual representations using a momentum teacher and self-distillation without the need for labeled data. The library functions as an image feature extractor and visual attention visualizer, allowing for the generation of high-dimensional vectors and the rendering of self-attention maps as heatmaps or videos to analyze model focus. It provides comprehensive tools for downstream vision evaluation, including linear probe classification, k-nearest neighbor categorizatio
DINOv2 is a self-supervised vision transformer foundation model designed to generate high-quality visual representations from raw image data. By leveraging large-scale unlabelled datasets, the framework learns to extract robust numerical embeddings that serve as inputs for various machine learning and analysis workflows. The model distinguishes itself through a teacher-student training framework that utilizes centered and sharpened soft probability distributions to align feature maps across multiple image crops. It incorporates a masking strategy that forces the model to reconstruct missing i
moco is a PyTorch implementation of momentum contrast designed for self-supervised visual representation learning. It serves as a research-based framework for extracting high-level image features from unlabeled datasets by maximizing the similarity between different views of the same image.
The main features of facebookresearch/moco are: Contrastive Learning Models, Contrastive Learning Frameworks, Visual Representation Learning Frameworks, Exponential Moving Average Weight Updates, Momentum Encoders, Self-Supervised Vision Representation Trainers, Cross-Batch Embedding Queues, Deep Learning Research.
Projects with overlapping indexed features include: google-research/simclr — This project is a self-supervised contrastive learning framework designed to train deep learning models to learn… lightly-ai/lightly — Lightly is a self-supervised learning framework and computer vision data curation tool designed to manage large image… facebookresearch/dino — This project is a PyTorch vision transformer framework designed for self-supervised learning. It implements a model… facebookresearch/dinov2 — DINOv2 is a self-supervised vision transformer foundation model designed to generate high-quality visual… facebookresearch/jepa — This is a PyTorch self-supervised learning framework designed to train models that learn visual representations from… lucidrains/dalle2-pytorch — This is a PyTorch implementation of a text-to-image model designed for synthesizing high-fidelity images from natural…