Software To Help You Turn Your Data Into AI
Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.
At the Active Community, we are elated to announce the release of Encord Active 0.1.75, marking a significant milestone in our ongoing commitment to delivering unparalleled user experiences. This isn't just any update; we've made changes to redefine how you interact with our platform. Gone is Streamlit, paving the way for a more agile, quicker, and responsive UI.
As always, our primary objective is to ensure that you have the smoothest experience possible, and with this latest release, we've achieved just that. Discover the transformative features and improvements we've meticulously integrated into Encord Active 0.1.75!
Encord Active provides a data-centric approach for improving model performance by helping you discover and correct erroneous labels through data exploration, model-assisted quality metrics, and one-click labeling integration.
With Encord Active you can:
Check out the project on GitHub, and hey, if you like it, leave us a 🌟🫡.
At the heart of the Encord Active 0.1.75 release is the evolution of our user interface. While Streamlit served us well as the primary UI in our initial stages, we recognized its limitations, particularly for an open-source tool designed for scalability and production-level performance. From constraints like its numerous dependencies and limited potential for custom frontend components to a lack of Google Colab integration, Streamlit posed challenges that hindered our vision.
We took this as a cue to redesign and introduce a new native UI that's faster and offers a significantly smoother experience. By transitioning to a dedicated backend-frontend setup, we've eradicated previous complications and set the stage for a more performant Encord Active in future iterations.
You'll now experience custom frontend components, seamless integration with Google Colab, a more responsive Explorer interface for delving deep into image datasets, enhanced usability, and swift loading times—a direct response to feedback from our community, who voiced concerns about sluggish interfaces with large datasets. By cutting ties with Streamlit and its inherent limitations, we have ushered in an era of increased speed and responsiveness—vital for effectively handling large computer vision datasets.
With this release, Encord Active gets a completely new look and feel. We think that it is fresh enough to get a brand new command:
encord-active start
The start command has now replaced the previous visualize command.
We’ve streamlined the prediction imports via the SDK. They follow the same fundamental structure, and the documentation should be clearer.
We have supercharged data tagging efficiency, achieving a remarkable 10x performance boost when tagging large amounts of data at once. Now, Encord Active can seamlessly handle large data batches simultaneously. This improvement improves your flow and makes data tagging lightning-fast.
While Streamlit was instrumental during our inception, its inherent challenges limited our scalability and adaptability. The all-new native UI in Encord Active 0.1.75 presents a clear, intuitive, responsive design built to serve our users' evolving needs.
A significant advantage of moving away from Streamlit is the seamless integration with Google Colab. This feature paves the way for smoother workflows, especially for those using Google Colab for their data and ML tasks. No more `ngrok` or `nginx` integrations are required!
We have put together a notebook for you to test this out. Run it directly from this notebook.
Exploring large image datasets? Our revamped Explorer is designed to ensure you navigate your datasets with unparalleled ease and speed. We have also added a button you can toggle under the Explorer tab to show or hide annotations in your images.
These allow for a more tailored user experience, giving you the tools and views you need without the fluff.
Importing predictions for videos had a bug that assigned predictions to the wrong frames in videos (and image groups). This is now resolved.
We have also addressed a crucial issue in our latest release concerning classification predictions. You can now trust that your classification predictions will be imported accurately and seamlessly.
We have optimized data migration processes to be more efficient. We've addressed the issue where object embeddings, a compute-intensive task, were unnecessarily calculated in certain scenarios. With this release, expect more streamlined migrations and reduced computational overhead.
In our previous releases, the Docker file was wrong, so the Docker version did not get released. We've rectified this oversight. With this fix, this release is now fully Docker-ready for smoother installations and deployments. We have also included `liggeos` in the Docker image during build when trying to set up a project. That fixes issue #598.
In our commitment to streamlining and simplifying, we've made a pivotal change in this release. We've eliminated the separate `encord-active-components` package, opting instead to directly distribute the build bundled with its essential components. This move ensures a more integrated and efficient deployment for you.
We've rectified an issue where signed URLs from AWS displayed "empty" cards in the explorer. Expect consistent and accurate data representation for your AWS-stored content.
We've seen the import process crash when importing projects with many/long videos (more than an hour of video in total). The issue is typically a lack of disk space from inflating videos into separate frames. We suggest using smaller projects with shorter videos for now. With one of the following releases, video support will be much more reliable and eliminate the need for inflating videos into frames.
Project subsetting is slow. We’re working to make this work much faster. We’ve also noticed complications when projects came from a local import (via the `init` command or `import --coco` command). We’re working on fixing this before the next release.
If you have added a filter on the Explorer that includes Data or Label tags and then remove tags from some of the shown items, the Explorer won’t remove the items immediately. A page refresh will, however, show the correct results.
Most of the features in previous versions of Encord Active are still there. Below, we’ve listed the features that are no longer available.
We plan to bring back the confusion matrix, and if you’re missing the export features, please let us know in the Active community.
This release wouldn't have been possible without the feedback and contributions from our community. We'd like to extend our heartfelt gratitude to everyone who played a part, especially those who highlighted the challenges with Streamlit and pushed for improved UI responsiveness. Your voices were instrumental in shaping this release.
pip install --upgrade encord-active
⚠️ Remember to run `encord-active start` and not `encord-active visualize` in your project directory.
Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.