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This is the development documentation of nidl (0.0.1)

Nidl
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Nidl
  • Quickstart
  • Examples
    • Model probing callback of embedding estimators
    • Presentation of the OpenBHB dataset
    • Self-Supervised Contrastive Learning with SimCLR on MNIST
    • Self-Supervised Learning with Barlow Twins
    • Visualization of metrics during training of PyTorch-Lightning models
    • Weakly Supervised Contrastive Learning with y-Aware
  • User guide
    • 1. Introduction
    • 2. What is nidl?
    • 3. Using nidl for the first time
    • 4. Applications to Neuroimaging
    • 5. Supervised Learning
    • 6. Self Supervised Learning
    • 7. Auto Encoders
    • 8. Model Probing
    • 9. Data Augmentation
    • 10. Pretrained Models
    • 11. Architectures
      • 11.1. Volume
      • 11.2. Surface
    • 12. Open Datasets
  • API References
    • nidl.estimators: Available estimators
      • nidl.estimators.BaseEstimator
      • nidl.estimators.ClassifierMixin
      • nidl.estimators.ClusterMixin
      • nidl.estimators.RegressorMixin
      • nidl.estimators.TransformerMixin
      • nidl.estimators.ssl.SimCLR
      • nidl.estimators.ssl.YAwareContrastiveLearning
      • nidl.estimators.ssl.BarlowTwins
      • nidl.estimators.ssl.DINO
      • nidl.losses.InfoNCE
      • nidl.losses.YAwareInfoNCE
      • nidl.losses.BarlowTwinsLoss
      • nidl.losses.DINOLoss
      • nidl.estimators.ssl.utils.ProjectionHead
      • nidl.estimators.ssl.utils.SimCLRProjectionHead
      • nidl.estimators.ssl.utils.YAwareProjectionHead
      • nidl.estimators.ssl.utils.BarlowTwinsProjectionHead
      • nidl.estimators.ssl.utils.DINOProjectionHead
      • nidl.estimators.autoencoders.VAE
      • nidl.losses.BetaVAELoss
    • nidl.volume.backbones: Available backbones
      • nidl.utils.weights.Weights
      • nidl.volume.backbones.AlexNet
      • nidl.volume.backbones.DenseNet
      • nidl.volume.backbones.ResNet
      • nidl.volume.backbones.ResNetTruncated
      • nidl.volume.backbones.densenet121
      • nidl.volume.backbones.resnet18_trunc
      • nidl.volume.backbones.resnet50
      • nidl.volume.backbones.resnet50_trunc
    • nidl.transforms: Available transformations
      • nidl.transforms.Transform
      • nidl.transforms.Identity
      • nidl.transforms.MultiViewsTransform
      • nidl.transforms.VolumeTransform
      • nidl.volume.transforms.preprocessing.RobustRescaling
      • nidl.volume.transforms.preprocessing.ZNormalization
      • nidl.volume.transforms.preprocessing.CropOrPad
      • nidl.volume.transforms.preprocessing.Resample
      • nidl.volume.transforms.preprocessing.Resize
      • nidl.volume.transforms.augmentation.RandomGaussianBlur
      • nidl.volume.transforms.augmentation.RandomGaussianNoise
      • nidl.volume.transforms.augmentation.RandomErasing
      • nidl.volume.transforms.augmentation.RandomFlip
      • nidl.volume.transforms.augmentation.RandomResizedCrop
      • nidl.volume.transforms.augmentation.RandomRotation
    • nidl.datasets: Available datasets
      • nidl.datasets.BaseImageDataset
      • nidl.datasets.BaseNumpyDataset
      • nidl.datasets.ImageDataFrameDataset
      • nidl.datasets.OpenBHB
    • nidl.callbacks: Available callbacks
      • nidl.callbacks.ModelProbing
      • nidl.callbacks.ModelProbingCV
      • nidl.callbacks.MetricsCallback
      • nidl.callbacks.BatchTypingCallback
    • nidl.metrics: Available metrics
      • nidl.metrics.pearson_r
      • nidl.metrics.alignment_score
      • nidl.metrics.uniformity_score
      • nidl.metrics.contrastive_accuracy_score
      • nidl.metrics.procrustes_similarity
      • nidl.metrics.procrustes_r2
      • nidl.metrics.kruskal_similarity
  • Glossary

Development

  • Contributing
  • Continuous integration
  • Maintenance
  • Changelog
  • Team
  • Versions
  • GitHub Repository
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11. ArchitecturesΒΆ

  • 11.1. Volume
  • 11.2. Surface
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11.1. Volume
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10. Pretrained Models
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