Package: encord-active
Documentation|Try it Now|Website|Blog|Join our Community❓ What is Encord Active?Encord Activeis an open-source toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling to supercharge model performance.Use Encord Active to:Test, validate, and evaluate your models with advanced error analysisGenerate model explainability reportsSurface, curate, and prioritize the most valuable data for labelingSearch through your data using natural language (beta feature)Find and fix dataset errors and biases (duplicates, outliers, label errors)InstallationThe simplest way to install the CLI is usingpipin a suitable virtual environment:pipinstallencord-activeWe recommend using a virtual environment, such asvenv:python3.9-mvenvea-venvsourceea-venv/bin/activate
pipinstallencord-activeencord-activerequirespython3.9.
If you have trouble installingencord-active, you find more detailed instructions on
installing ithere. If just want to see it in action try outthis Colab notebook.🖥️ Supported Platforms🚨 Note: Currently, this library does not support Linux 🚫🐧.Supported platforms include:WindowsmacOS🐧 Linux SupportWe understand the importance of Linux in many development environments. We are actively exploring ways to bring Linux support in the future 🔍. If you're interested in contributing or have suggestions, please read thecontribution guidelines🤝.👋 QuickstartGet started immediately by sourcing your environment and running the code below.
This downloads a small dataset and launches the Encord Active App for you to explore:encord-activequickstartor you can use:dockerrun-it--rm-p8000:8000-v${PWD}:/dataencord/encord-activequickstartAfter opening the UI, we recommend you to head to theworkflow documentationto see some common workflows.💡 When to use Encord Active?Encord Active helps you understand and improve your data, labels, and models at all stages of your computer vision journey.Whether you've just started collecting data, labeled your first batch of samples, or have multiple models in production, Encord Active can help you.🔖 DocumentationOur full documentation is availablehere. In particular, we recommend checking out:Getting StartedImportsWorkflowsUser GuidesCLI Documentation⬇️ Download a sandbox datasetAnother way to quickly get familiar with Encord Active is to download a dataset from its sandbox.
The download command will ask which pre-built dataset to use and will download it into a new directory in the current working directory.encord-activedownloadcd/path/of/downloaded/project
encord-activestartThe app should then open in the browser.
If not, navigate tolocalhost:8000.
Ourdocscontain more information about what you can see in the page.Import your datasetQuick import DatasetTo import your data (without labels) save your data in a directory and run the command:# within venvencord-activeinit/path/to/data/directoryA project will be created using the data in the directory.To start the project run:cd/path/to/project
encord-activestartYou can find more details on theinitcommand in thedocumentation.Import from COCOTo import your data, labels, and predictions from COCO, save your data in a directory and run the command:# install COCO extras(ea-venv)$python-mpipinstallencord-active[coco]# import samples with COCO annotationsencord-activeimportproject--coco-i./images-a./annotations.json# import COCO model predictionsencord-activeimportpredictions--cocoresults.jsonImport from the Encord platformThis section requiressetting up an ssh keywith Encord, so slightly more technical.To import an Encord project, use this command:encord-activeimportprojectThe command will allow you to search through your Encord projects and choose which one to import.⭐ Concepts and featuresQuality metrics:Quality metrics are applied to your data, labels, and predictions to assign them quality metric scores.
Plug in your own or rely on Encord Active's prebuilt quality metrics.
The quality metrics automatically decompose your data, label, and model quality to show you how to improve your model performance from a data-centric perspective.
Encord Active ships with 25+ metrics and more are coming;contributionsare also very welcome.Core features:Data ExplorationData Outlier detectionLabel Outlier detectionObject Detection/segmentation Model DecompositionClassification Model DecompositionSimilarity SearchData & Label TaggingVisualize TP/FP/FNAnd much more!Visit ourdocumentationto learn more.Supported data:Data TypesLabelsProject sizesjpg✅Bounding Boxes✅Images25.000png✅Polygons✅Videos *25.000 framestiff✅Segmentations✅mp4*✅Classifications✅Polylines🟡* Requires an Encord Annotate account🧑🏽‍💻Development🛠 Build your own quality metricsEncord Active is built with customizability in mind. Therefore, you can easily build your own custom metrics 🔧.
See theWriting Your Own Metricpage in the docs for details on this topic.If you need help or guidance feel free to ping us in ourSlack workspace!👪 Community and supportJoin our community on Slackto connect with the team behind Encord Active.
Also, feel free tosuggest improvements or report problemsvia GitHub issues.🎇 ContributionsIf you're using Encord Active in your organization, please try to add your company name to theADOPTERS.md. It really helps the project to gain momentum and credibility. It's a small contribution back to the project with a big impact.If you want to share your custom metrics or improve the tool, please see ourcontributing docs.🦸 Contributors@Javi LeguinaLicenceThis repository is published under the Apache 2.0 licence.