VISSL

VISSL is a computer VIsion library for state-of-the-art Self-Supervised Learning research with PyTorch. VISSL aims to accelerate research cycle in self-supervised learning: from designing a new self-supervised task to evaluating the learned representations. Key features include:

• Reproducible implementation of SOTA in Self-Supervision: All existing SOTA in Self-Supervision are implemented - SwAV, SimCLR, MoCo(v2), PIRL, NPID, NPID++, DeepClusterV2, ClusterFit, RotNet, Jigsaw. Also supports supervised trainings.

• Benchmark suite: Variety of benchmarks tasks including linear image classification (places205, imagenet1k, voc07), full finetuning, semi-supervised benchmark, nearest neighbor benchmark, object detection (Pascal VOC and COCO).

• Model Zoo: Over 60 pre-trained self-supervised model weights.

Tutorials

Get started with VISSL by trying one of the Colab tutorial notebooks.

• Train SimCLR on 1-gpu
• Extracting Features from a pretrained model
• Benchmark task: Full finetuning on ImageNet-1K
• Benchmark task: Linear image classification on ImageNet-1K
• Large scale training (fp16, LARC, ZeRO)