VISSL
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About
VISSL is a comprehensive library designed for self-supervised learning, employing state-of-the-art methods for image analysis. Built on PyTorch, it allows users to utilize its wide array of components effectively. The library includes reproducible implementations of various self-supervision approaches such as SimCLR, MoCo, PIRL, and SwAV, along with capabilities for supervised training. VISSL also offers numerous benchmark tasks for evaluating models across various categories such as linear image classification, semi-supervised learning, low-shot learning, and object detection. The system is scalable, providing seamless model training on single or multiple GPUs and across larger datasets with support for advanced techniques like FP16 and LARC.
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Task
Features
• state-of-the-art self-supervised learning methods
• supports supervised trainings
• scalable training across single and multi-gpu setups
• multiple benchmark tasks for model evaluation
• reproducible implementations of popular models
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