lightly

First Steps

  • Lightly at a Glance
    • How Lightly Works
      • Data and Transformations
      • Training
      • Embeddings
    • Lightly in Three Lines
    • What’s next?
  • Benchmarks
    • CIFAR10
  • Getting Started
    • Supported Python versions
    • Installing Lightly
    • Dependencies
    • Next Steps
  • Command-line tool
    • Train a model using the CLI
    • Create embeddings using the CLI
    • Upload data using the CLI
    • Upload embeddings using the CLI
    • Download data using the CLI
  • Advanced
    • Augmentations
    • Models
    • Losses
      • Memory Bank
    • Extracting specific Video Frames
  • The Lightly Platform
    • Basic Concepts
      • Dataset
        • Upload Samples
      • Tag
      • Embedding
        • Obtaining Good Embeddings
    • Create a Dataset
    • Upload Images
    • Upload Embeddings
      • Sampling
    • Dataset Identifier
    • Authentication API Token

Tutorials

  • Python Package
    • Tutorial 1: Structure Your Input
      • Supported File Types
        • Images
        • Videos
      • Image Folder Datasets
        • Flat Directory Containing Images
        • Directory with Subdirectories Containing Images
      • Video Folder Datasets
      • Embedding Files
        • Advanced usage of Embeddings
      • Next Steps
    • Tutorial 2: Train MoCo on CIFAR-10
      • Imports
      • Configuration
      • Setup data augmentations and loaders
      • Create the MoCo Lightning Module
      • Create the Classifier Lightning Module
      • Train the MoCo model
    • Tutorial 3: Train SimCLR on Clothing
      • Imports
      • Configuration
      • Setup data augmentations and loaders
      • Create the SimCLR model
      • Train the Embedding
      • Visualize Nearest Neighbors
      • Color Invariance
    • Tutorial 4: Train SimSiam on Satellite Images
      • Imports
      • Configuration
      • Setup data augmentations and loaders
      • Create the SimSiam model
      • Train SimSiam
      • Scatter Plot and Nearest Neighbors
    • Tutorial 5: Custom Augmentations
      • Imports
      • Configuration
      • Setup custom data augmentations
      • Setup dataset and dataloader
      • Create the MoCo model
      • Setup criterion and optimizer
      • Train MoCo with custom augmentations
      • Evaluate the results
  • Platform
    • Tutorial 1: Curate Pizza Images
      • What you will learn
      • Requirements
      • Upload the data
      • Filter the dataset using metadata
      • Download the curated dataset
      • Training a model using the curated data

Python API

  • lightly
  • .core
  • lightly.api
    • .upload
    • .download
    • .utils
  • lightly.cli
    • .lightly_cli
    • .train_cli
    • .embed_cli
    • .upload_cli
    • .download_cli
    • .config.config.yaml
      • Overwrites
      • Additional Arguments
      • Default Settings
  • lightly.data
    • .collate
    • .dataset
  • lightly.embedding
    • .embedding
  • lightly.loss
    • .ntx_ent_loss
    • .sym_neg_cos_sim_loss
    • .memory_bank
  • lightly.models
    • .resnet
    • .simclr
    • .moco
    • .simsiam
    • .zoo
  • lightly.transforms
    • .gaussian_blur
    • .rotation
  • lightly.utils
    • .io
    • .embeddings_2d

On-Premise

  • Docker
    • Setup
      • Analytics
      • Licensing
      • Download image
    • First Steps
      • Storage Access
      • Embedding and Sampling a Dataset
      • Train a Self-Supervised Model
      • Accessing Lightly Input Parameters
      • Sampling from Embeddings File
      • Sampling from Video Files
      • Removing Exact Duplicates
      • Reporting
        • Docker Output
        • Evaluation of the Sampling Proces
    • Advanced
      • Meta Information
        • Mask Samples
        • Use Pre-Selected Samples
        • Custom Weak Labels
      • Datapool
        • How It Works
        • Usage
    • Configuration
      • List of Parameters
      • Choosing the Right Parameters
      • Increase I/O Performance
    • Examples
      • Extract Diverse Video Frames
        • Using ffmpeg
        • Using Lightly Docker
      • ImageNet
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  • »
  • Python Module Index

Python Module Index

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- lightly
    lightly.api
    lightly.api.download
    lightly.api.upload
    lightly.api.utils
    lightly.cli
    lightly.cli.download_cli
    lightly.cli.embed_cli
    lightly.cli.lightly_cli
    lightly.cli.train_cli
    lightly.cli.upload_cli
    lightly.core
    lightly.data
    lightly.data.collate
    lightly.data.dataset
    lightly.embedding
    lightly.embedding.embedding
    lightly.loss
    lightly.models
    lightly.models.moco
    lightly.models.resnet
    lightly.models.simclr
    lightly.models.simsiam
    lightly.models.zoo
    lightly.transforms
    lightly.transforms.gaussian_blur
    lightly.transforms.rotation
    lightly.utils
    lightly.utils.embeddings_2d
    lightly.utils.io

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