The open sourceactive learning toolkit for computer vision
A customizable toolkit for your data, labels, and models. Encord Active helps you find failure modes in your models, prioritize high-value data for labelling, and drive smart data curation to improve model performance.
- 🚀The fastest way to debug your dataset and improve model performance
- 🤠Built for ML Engineers and Data Scientists
- ⚡One line of code to get started to import you data, labels, and model predictions
$ pip install encord-active
$ encord-active quickstartEnhance data quality and model performance
Encord Active has been designed as a all-in-one open source toolkit for improving your data quality and model performance. Use the intuitive UI to explore your data or access all the functionalities programmatically.
Ensure high quality training data for computer vision
Discover errors, outliers, and edge-cases within your data - all in one open source toolkit.
Understand your data distribution
Get a high level overview of your data distribution, explore it by customizable quality metrics, and discover any anomalies.
Surface outliers
Quickly find image outliers and tag them for further investigation.
Find similar images
Use powerful similarity search to find more examples of edge-cases or outliers.
Ensure your annotations meet your quality standards
Save time and improve label quality by understanding your label distribution, label errors, and annotator performance.
Understand your label distribution
Explore your label distribution by label classes, quality metrics, and custom metrics to uncover hidden gaps in your data.
Find outliers in your labels
Quickly find label outliers and tag them for further investigation.
Explore annotator performance
Analyze the speed and performance of your annotators.
Gain visibility into model performance and failure modes
Benefit from intelligent model evaluation features based on quality metrics that allows you to find model failure modes and target your data collection and labeling on high-value data.
Find model failure modes
Drill down into each quality metrics to discover how your model is performing and find hidden model failure modes.
Correlate model performance
Correlate model performance to each quality metric to understand what data and label characteristics impact your model performance.
Find & fix label errors
Find label errors and send them to re-labeling immediately to improve the quality of your training data.
