data2vec-vision and data2vec-pytorch
These two tools are competitors, as both provide PyTorch implementations of the Data2Vec self-supervised learning approach, with B being a more general implementation that also supports speech and text.
About data2vec-vision
Guillem96/data2vec-vision
PyTorch implementation of Data2Vec self-supervised approach for vision use cases.
This project helps machine learning engineers build powerful computer vision models for tasks like image recognition or object detection, even with limited labeled data. It takes a collection of images and outputs a trained model that can extract meaningful features from new images. This is for machine learning engineers who want to pre-train image understanding models more efficiently.
About data2vec-pytorch
ashutosh1919/data2vec-pytorch
Ready to run PyTorch implementation of Data2Vec 2.0: Highly efficient self-supervised representation learning for vision, speech and text.
This project helps machine learning researchers and engineers quickly set up and train self-supervised models for various data types. It takes raw image datasets like ImageNet, speech audio like LibriSpeech, or text corpora like OpenWebText as input. The output is a trained representation model ready for further downstream tasks in computer vision, speech recognition, or natural language processing.
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