Documentation | Try it Now | Website | Blog | Join our Community
Encord Active is 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 analysis
- Generate model explainability reports
- Surface, curate, and prioritize the most valuable data for labeling
- Search through your data using natural language (beta feature)
- Find and fix dataset errors and biases (duplicates, outliers, label errors)
The simplest way to install the CLI is using pip in a suitable virtual environment:
pip install encord-activeWe recommend using a virtual environment, such as venv:
python3.9 -m venv ea-venv
source ea-venv/bin/activate
pip install encord-active
encord-activerequires python3.9. If you have trouble installingencord-active, you find more detailed instructions on installing it here. If just want to see it in action try out this Colab notebook.
Get 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-active quickstartor you can use 
docker run -it --rm -p 8000:8000 -v ${PWD}:/data encord/encord-active quickstartAfter opening the UI, we recommend you to head to the workflow documentation to see some common workflows.
Encord Active helps you understand and improve your data, labels, and models at all stages of your computer vision journey.
Whether you've just started collecting data, labeled your first batch of samples, or have multiple models in production, Encord Active can help you.
Encord Active (EA) is available in two versions:
- Encord Active Cloud 🌥️
- Encord Active OS 💻
Active Cloud is tightly integrated with Encord Annotate, with Active Cloud and Annotate being hosted by Encord--you can signup and start using Active without installing anything.
Or self-host and start using the open source toolkit locally ⬇️.
Our full documentation is available here. In particular, we recommend checking out:
Another way to quickly get familiar with Encord Active is to download a dataset from its sandbox. The download command will ask which pre-built dataset to use and will download it into a new directory in the current working directory.
encord-active download
cd /path/of/downloaded/project
encord-active startThe app should then open in the browser.
If not, navigate to localhost:8000.
Our docs contain more information about what you can see in the page.
To import your data (without labels) save your data in a directory and run the command:
# within venv
encord-active init /path/to/data/directoryA project will be created using the data in the directory.
To start the project run:
cd /path/to/project
encord-active startYou can find more details on the init command in the documentation.
To import your data, labels, and predictions from COCO, save your data in a directory and run the command:
# install COCO extras
(ea-venv)$ python -m pip install encord-active[coco]
# import samples with COCO annotations
encord-active import project --coco -i ./images -a ./annotations.json
# import COCO model predictions
encord-active import predictions --coco results.jsonThis section requires setting up an ssh key with Encord, so slightly more technical.
To import an Encord project, use this command:
encord-active import projectThe command will allow you to search through your Encord projects and choose which one to import.
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; contributions are also very welcome.
- Data Exploration
- Data Outlier detection
- Label Outlier detection
- Object Detection/segmentation Model Decomposition
- Classification Model Decomposition
- Similarity Search
- Data & Label Tagging
- Visualize TP/FP/FN
- And much more!
Visit our documentation to learn more.
| Data Types | Labels | Project sizes | |||
|---|---|---|---|---|---|
jpg |
✅ | Bounding Boxes | ✅ | Images | 25.000 |
png |
✅ | Polygons | ✅ | Videos * | 25.000 frames |
tiff |
✅ | Segmentations | ✅ | ||
mp4 * |
✅ | Classifications | ✅ | ||
| Polylines | 🟡 |
* Requires an Encord Annotate account
Don't want to host Encord Active on your server? Or are you loading >25k images for your project? Check out Active Cloud extending features from Encord Active open source to help you:
✅ Curate datasets better with Collections
✅ Evaluate models with in-built and custom metrics
✅ Perform Natural Language Search
✅ Search your dataset by images
✅ Integrate with annotation platform
✅ Filter by metadata
Encord Active is built with customizability in mind. Therefore, you can easily build your own custom metrics 🔧. See the Writing Your Own Metric page in the docs for details on this topic.
If you need help or guidance feel free to ping us in our Slack workspace!
Join our community on Slack to connect with the team behind Encord Active. Also, feel free to suggest improvements or report problems via GitHub issues.
If you're using Encord Active in your organization, please try to add your company name to the ADOPTERS.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 our contributing docs.
@misc{Encord2024,
author = {Encord},
title = {Encord Active},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/encord-team/encord-ctive}},
}
This repository is published under the Apache 2.0 licence.