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PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882
The main features of facebookresearch/swav are: Advanced Learning, Self-Supervised Learning.
Projects with overlapping indexed features include: facebookresearch/dino — This project is a PyTorch vision transformer framework designed for self-supervised learning. It implements a model… facebookresearch/vissl — VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images. facebookresearch/jepa — This is a PyTorch self-supervised learning framework designed to train models that learn visual representations from… timeseriesai/tsai — tsai is a deep learning library for time series classification, regression, and forecasting. Built on PyTorch and… brightmart/albert_zh — This project is an implementation of the ALBERT language model architecture, providing a framework for training and… atcold/pytorch-deep-learning-minicourse — This is an educational curriculum for building and training neural networks using PyTorch. It serves as a deep…
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
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.
This is a PyTorch self-supervised learning framework designed to train models that learn visual representations from video. It implements a joint-embedding predictive architecture that extracts spatio-temporal features by predicting missing regions of a signal within a latent representation space rather than reconstructing raw pixels. The project includes a latent space visualization tool that uses a conditional diffusion model to decode feature-space predictions back into pixels. This allows for the verification of learned representations by transforming abstract predictions into interpretab
tsai is a deep learning library for time series classification, regression, and forecasting. Built on PyTorch and fastai, it provides a framework for assigning labels to sequential data, predicting future values in univariate or multivariate sequences, and training representations on unlabeled data through self-supervised learning. The library distinguishes itself with specialized temporal engineering and scaling capabilities. It includes tools for cyclical temporal encoding to capture seasonal patterns and online window slicing to process datasets larger than available memory. It also suppor