Spacenet tutorial full#
Spacenet tutorial code#

Model performance evaluation tools for the SpaceNet IoU metric (APLS coming soon!) The ability to use pre-trained or freshly initialized SpaceNet models, as well as your own custom models These functions are currently implemented with both PyTorch and TensorFlow backends. Training mask creation functions, with the option to create custom width edge masks, building footprint masks, road network masks, multi-class masks, and even masks which label narrow spaces between objects.Īll required deep learning functionality, from augmentation (including >3 channel imagery tools!) to data ingestion to model training and inference to evaluation during training. The solaris Python API provides every functionality needed to perform deep learningĬustomizable imagery and vector label tiling, with different size and coordinate system options. If these relatively narrow use cases don’t cover your needs, the solaris python

Spacenet tutorial full#

Running a full deep learning pipeline using the solaris CLIĮvaluating prediction quality on SpaceNet data with the solaris CLI On creating configuration files and running the CLI can be found below.Ĭreating reference files to help solaris find your imageryĬreating training masks with the solaris CLI Solaris does all the work to make your analysis pipeline fit together.

Spacenet tutorial code#

Instead of having to write code to help solarisįind your data, you just make basic edits to a configuration file template, then Using vector labels to running an entire deep learning pipeline throughĮvaluating model performance. Terminal or command prompt for standard operations, from creating training masks Without writing a single line of python code.Īfter installing Solaris, you can run simple commands from a You can run training and/or prediction on overhead imagery using SpaceNet models The command line interface (CLI) is the simplest way to use Solaris. Example 3 Follow-Up: Masking and Multimodal Datasets.Example 1 Follow-Up: Building a Reusable Class.Example 2 Follow-Up: Parallel Processing.Solaris Multimodal Preprocessing Library.Initialize a few more functions for scoring our results.Post-processing- binarize our masks and convert them to polygons.Specify our directories for post-processing.Create a csv file that lists our images and our masks for training and testing.Calculate some basic statistics for z-scoring (normalizing) our imagery.Dialate our masks to increase the size of our labels.Tile our masks and convert them to GeoTiffs.Specify our directories for pre processing.Mapping vehicles with solaris and the cowc dataset.Creating reference CSVs for model training and inference.Ground truth and prediction data formats.Using the solaris CLI to score model performance.Running a deep learning pipeline with the solaris CLI.Batch mask creation using the solaris CLI.Using the solaris CLI to make training masks.Training included SpaceNet models with the solaris Python API.Training your own custom model using solaris.Tiling imagery and labels using the solaris Python API.Creating training masks with the solaris python API.

Converting model outputs to vector format using the Python API.Scoring model performance with the solaris python API.Converting GeoJSON labels to COCO-formatted labels using Solaris.Python API: Use solaris to accelerate model development.Command line: train or test models performance with a single command.Installing from GitHub using a conda environment and pip.