Self-Supervised Learning

Related tags

Deep Learningmaemocoself-supervised-learningbyolsimclrbeit
Overview

Self-Supervised Learning

Features

self_supervised offers features like

  • modular framework
  • support for multi-gpu training using PyTorch Lightning
  • easy to use and written in a PyTorch like style
  • supports custom backbone models for self-supervised pre-training

Supported Models

Supported Loss Function

Benchmarks

Currently implemented models and their accuracy on cifar10 and imagenette.

ImageNette

Model Epochs Batch Size Test Accuracy
MoCo 800 256 0.827
SimCLR 800 256 0.847
SimSiam 800 256 0.827
BarlowTwins 800 256 0.801
BYOL 800 256 0.851

Cifar10

Model Epochs Batch Size Test Accuracy
MoCo 200 128 0.83
SimCLR 200 128 0.78
SimSiam 200 128 0.73
BarlowTwins 200 128 0.84
BYOL 200 128 0.85
MoCo 200 512 0.85
SimCLR 200 512 0.83
SimSiam 200 512 0.81
BarlowTwins 200 512 0.78
BYOL 200 512 0.84
MoCo 800 128 0.89
SimCLR 800 128 0.87
SimSiam 800 128 0.80
MoCo 800 512 0.90
SimCLR 800 512 0.89
SimSiam 800 512 0.91

Tutorials

Want to jump to the tutorials and see lightly in action?

Further Reading

Self-supervised Learning:

Owner
Robin
Machine learning Statistics
Robin
GitHub Repository
Implementation of ICLR 2020 paper "Revisiting Self-Training for Neural Sequence Generation"

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Weakly Supervised Segmentation by Tensorflow. Implements semantic segmentation in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).

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ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers

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Lidar with Velocity A robust camera and Lidar fusion based velocity estimator to undistort the pointcloud. related paper: Lidar with Velocity : Motion

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PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.

GCResNet PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021. The code will

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