solaris 0.4.0
  • Tutorials
  • API
  • Site
      • Installing solaris
      • An introduction to solaris
      • Pretrained models available in solaris
      • Solaris API summary
      • Solaris Tutorials and Cookbook
  • Page
      • Installing solaris
        • Prerequisites
        • Installing from GitHub using a conda environment and pip
        • Installing with only pip
      • An introduction to solaris
        • What is solaris?
        • Why should I use solaris?
        • How do I use solaris?
          • Command line: train or test models performance with a single command
          • Python API: Use solaris to accelerate model development
      • Pretrained models available in solaris
        • Model details
        • Training details
      • Solaris API summary
        • Complete submodule documentation
        • Submodule summaries
          • solaris.tile API reference
          • solaris.raster API reference
          • solaris.vector API reference
          • solaris.preproc API reference
          • solaris.nets API reference
          • solaris.eval API reference
          • solaris.utils API reference
          • solaris.data API reference
        • CLI commands
      • Solaris Tutorials and Cookbook
        • Converting GeoJSON labels to COCO-formatted labels using Solaris
          • Syntax
          • Examples
          • Still have questions?
        • Scoring model performance with the solaris python API
          • CSV Eval
          • GeoJSON Eval
        • Converting model outputs to vector format using the Python API
        • Creating training masks with the solaris python API
          • Polygon footprints
          • Polygon outlines
          • Polygon contact points
          • Road network masks
        • Tiling imagery and labels using the solaris Python API
          • Tiling the imagery
        • Training your own custom model using solaris
        • Training included SpaceNet models with the solaris Python API
        • Using the solaris CLI to make training masks
          • Single mask creation with the CLI
          • Batch mask creation using the solaris CLI
        • Running a deep learning pipeline with the solaris CLI
        • Using the solaris CLI to score model performance
          • Ground truth and prediction data formats
          • Scoring functions in the solaris CLI
        • Creating reference CSVs for model training and inference
          • Training Data CSV
          • Validation Data CSV
          • Inference Data CSV
          • Using these files
        • Creating the YAML Configuration File
          • Helpful Resources
          • The elements of the Config file
          • How the config file is processed
        • Mapping vehicles with solaris and the cowc dataset
          • Specify our directories for pre processing
          • Initialize a tiling function
          • Orgainze our data
          • Tile our masks and convert them to GeoTiffs
          • Dialate our masks to increase the size of our labels
          • Calculate some basic statistics for z-scoring (normalizing) our imagery
          • Hold out a city for testing
          • Review some of our masks and images
          • Create a csv file that lists our images and our masks for training and testing
          • Edit your .yml file and begin training your model
          • Time to inference
          • Specify our directories for post-processing
          • Post-processing- binarize our masks and convert them to polygons
          • Check out our results
          • Initialize a few more functions for scoring our results.
          • Score our results
        • Solaris Multimodal Preprocessing Library
          • Tutorial Part 2: Branching
          • Example 2 Follow-Up: Parallel Processing
        • Solaris Multimodal Preprocessing Library
          • Tutorial Part 1: Pipelines
        • Example 1: A Simple Pipeline
        • Example 1 Follow-Up: Building a Reusable Class
        • Solaris Multimodal Preprocessing Library
          • Tutorial Part 3: SAR
        • Example 3 Follow-Up: Masking and Multimodal Datasets
        • Epilogue
        • The command line interface
        • The Python API
        • Reference
        • Index

    Python Module Index

    s
     
    s
    - solaris
        solaris.data.coco
        solaris.eval.base
        solaris.eval.challenges
        solaris.eval.iou
        solaris.eval.pixel
        solaris.nets._keras_losses
        solaris.nets._torch_losses
        solaris.nets.callbacks
        solaris.nets.datagen
        solaris.nets.infer
        solaris.nets.losses
        solaris.nets.metrics
        solaris.nets.model_io
        solaris.nets.optimizers
        solaris.nets.torch_callbacks
        solaris.nets.train
        solaris.nets.transform
        solaris.nets.zoo
        solaris.preproc.image
        solaris.preproc.label
        solaris.preproc.pipesegment
        solaris.preproc.sar
        solaris.raster.image
        solaris.tile.raster_tile
        solaris.tile.vector_tile
        solaris.utils.config
        solaris.utils.core
        solaris.utils.data
        solaris.utils.geo
        solaris.utils.io
        solaris.utils.raster
        solaris.utils.tile
        solaris.vector.graph
        solaris.vector.mask
        solaris.vector.polygon

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