TLDR Workflows leaves Beta! Label snapshot versioning AI support is now in the platform - instant-access to support Encord Active improves Data Curation, Model Observability Introducing Encord Labs Encord @ RSNA - Come and see us at Booth #3772! All this and more! Read on to make your day. Workflows Leaves Beta We are thrilled to announce that our highly-anticipated feature, Workflows, has officially transitioned from beta to general availability! This milestone could not have been achieved without the invaluable feedback from our dedicated users throughout the beta phase. Workflows are designed to give you full control of the annotation process, ensuring a blend of high performance, usability, and extensibility that scales with the rapid pace and change of the AI industry. Some of the major improvements are: Performance: Handle larger projects efficiently (a tenfold increase from the previous benchmark), with significant speed enhancements across the platform. Usability: A new drag-and-drop UI simplifies workflow creation and the updated queue gives you full insight into the progress of your project. Extensibility: Advanced routing, better review functionality, and integration with Encord Active tailored to evolving AI demands. Editor Power Ups Workflow scalability means more tasks and more labels in your annotation projects. We're also juicing up the editor to be more performant -- which means more labels per task, faster. Backend improvements mean your data will save faster and more seamlessly, and we're introducing raised limits on labels per task to benefit from those improvements as well -- contact us to work with more data per task! Arriving soon are further performance improvements to enhance the user experience when dealing with many objects and complex label timelines. This all adds up to create a more natural pre-processing and editing experience, even on long, label intense, video annotation workloads. Exciting! AI Support We understand that searching our documentation isn’t always your first thought when you need to learn about the platform. To address this, we've integrated AI support directly into our platform, ensuring you have quick access to the assistance you need, precisely when needed.  Whether you're onboarding for the first time, looking for a quick refresher on using the Label Editor, or need help understanding terminology, our AI assistant is here to help. It is regularly trained on all our platforms & SDK documentation, enabling it to provide intelligent and up-to-date responses to any questions you may have about our application! Active Improves Data Curation and Model Evaluation We know that curating the best images, frames from a video, or slices from a scan is a daunting, difficult, and time-intensive task. First, ensuring that your dataset is free of outliers, duplicates, and irrelevant images, and second, selecting the best samples is crucial for building robust and performant models. Encord is your trusted partner along your journey and based on your feedback we have designed Active's new Explorer to simplify this process, incorporating best practices into intuitive user journeys: Automated data quality checks: Active automatically identifies potential issues in your datasets, such as duplicates, blurry images, or corrupted frames. By filtering out these problematic frames, you can reduce annotation costs and prevent detrimental effects on your model's performance. Intelligent curation: Use Active to curate a balanced and diverse dataset. Whether you're establishing a dataset for an initial model run or curating targeted data for critical edge cases or blind spots, Active has a tailored workflow ready for you. After your data is annotated and your model is trained, Encord Active simplifies the shift to evaluation. Simply import your model predictions and access a detailed analysis of your model’s performance, with options to break it down by class, data collections, and splits such as train, test, and validation. You can also use the Explorer to investigate your prediction types following a series of best-practice workflows: Prediction inspection: Use the Explorer to delve into the types of model predictions – True Positives (TP), False Positives (FP), and False Negatives (FN), to understand your model's accuracy and behavior. Spot and address blind spots: When an edge case or a blind spot is detected, Active's similarity search allows you to surface and curate additional samples from your unlabeled data pool that resemble these critical cases. Continuous improvement cycle: Integrate these new samples into your annotation workflow, retrain your model, and directly compare performance improvements against previously identified edge cases. Label Snapshot Versioning Labeling training data is, like the model training process it supports, an iterative process. You’ve asked for ways to snapshot your progress — whether it’s to save a checkpoint before re-labeling, check-in progress as you work through a large project, or name different subsets for purposes such as training, testing, and validation. We’ve listened, and are happy to introduce label versioning for workflow projects. Navigate to the labels tab, select your tasks, and press ‘Save new version’ — you can refer to these snapshots by name and time. Initially, we’re rolling out support for exporting labels from saved checkpoints, but look out for coming improvements such as restoring to different projects. As always, let us know how it helps and what more we can do to enhance your AI initiatives with labels and label set management tools!  Opt-in to Beta Features Faster with Encord Labs Many of you have shown interest in working closely with our product development team and helping us create the best features — as such, we’re very happy to be introducing Encord Labs! Encord Labs will give you access to features at the bleeding edge, but give you control over how features appear in the platform. This means you will get all the power of rapidly evolving technology with none of the risks. Getting in on the ground floor means you can shape how features evolve faster, helping us ensure we build with tight customer feedback in mind. Encord Labs will be rolling out several select features in Q4 — contact us if you’re interested or would like to join our collaborative early tester program! Thanks for reading, feel free to email product@encord.com with any questions or suggestions, and let us know if you're attending RSNA 2023!

TLDR Ontologies play a central role in solving Computer Vision problems, offering structured data organization, consistent labeling, and giving your models the necessary information to perform as expected.  An ontology can include different object types, classifications, and descriptive attributes, each annotated in distinct ways. In Encord you can create arbitrary complex, detailed, and nested ontologies, with Dynamic Classification adapting video annotations in real-time. A good ontology ensures ongoing consistency and is essential for the success of your project. Complex ontologies have proven invaluable in complex computer vision tasks, as seen in real-world applications such as medical diagnosis, insurance claims, sports analytics, and agriculture.  Understanding Ontologies Ontologies provide a structured framework for categorizing data, ensuring uniform terminology, hierarchical organization, and consistent labeling. Annotations within ontologies conform to predefined concepts, relationships, properties, and attributes. This adherence significantly elevates data quality while reducing potential for errors.  Ontologies are divided into two key components: Objects Objects represent individual entities or instances of the same entity within a domain. These are concrete elements that can be classified or categorized based on their attributes and characteristics. For example, in a medical ontology, individual patients, diseases, or forms of medication would be considered objects.  When it comes to annotating these objects in your projects, various object annotation types can be employed: Bounding Boxes Rectangular boxes around objects of interest within images or frames. Use Cases: Object detection (identifying and localizing objects within images), face recognition (enclosing faces), and vehicle tracking (tracking vehicles in surveillance footage). Polygon Annotators outline objects using polygons with multiple vertices. Use Cases: Semantic segmentation (precisely delineating object boundaries in images), building footprint extraction from satellite imagery, annotating irregularly shaped objects. Keypoint Specific key points or landmarks on objects. Use Cases: Pose estimation (annotating key points on human body parts), facial landmark detection (marking key points on a face), and hand gesture recognition. Bitmask or Pixel-Level Annotation Annotators can iteratively define regions of interest by assigning specific ‘bits’ or values to pixels, effectively categorizing them within the selected areas. Use Cases: Semantic segmentation (pixel-wise object labeling in images), medical image segmentation (identifying structures in medical scans). Classification Classification represents broader categories or classes within the ontology. Using the medical ontology example, classifications could include categories like "Patients," "Diseases," or "Medication”. Ontologies refine classification tasks by organizing classes hierarchically, enhancing precision in labeling and prediction. Unlike objects, classifications are applied to the entire frame and they typically fall into one of the following categories: Checklist Single option from a list of predefined categories. Use Cases: Scene classification (e.g., categorizing images as "indoor" or "outdoor"), sentiment analysis (e.g., categorizing reviews as "positive," "negative," or "neutral"). Radio Multiple options from a list of predefined categories. Use Cases: Image tagging (e.g., tagging an image with multiple labels like "beach," "sunset," and "ocean"), document classification (e.g., labeling documents with multiple relevant topics). Text Freely input text to describe or classify data. Use Cases: Text classification (e.g., categorizing text documents into custom-defined categories), user-generated content moderation (e.g., labeling text as "spam" or "not spam"). Building Your Own Ontology with Encord Create New First, navigate to the ‘Ontologies’ section of Encord and create a new ontology using the + New Ontology button. For this walkthrough, we will create an ontology for annotating humans for a semantic segmentation task. Configure Ontology Now, build your ontology structure by adding all the objects and classifications necessary for the creation of your dataset.  As you can see, to annotate the types of clothes a human is wearing we have created a very detailed ontology. The objects with “*” denote the objects which are required to be annotated. This prioritization enhances data quality, annotation efficiency, and project scalability. Utilizing Encord's Nested Classification feature, our ontology design extends beyond just identifying primary clothing items like "shirts," "pants," and "shoes." It dives into finer details, encompassing attributes such as "accessories" and "sleeve length." This hierarchical approach doesn't stop at surface categorizations; it goes deeper to capture nuanced attributes. The result is a highly informative and granular dataset, perfect for demanding computer vision tasks. Moreover, Encord offers the flexibility to create as many nested levels as your project requires, ensuring that your annotation process adapts seamlessly to the complexity of the data. In the case of annotating videos, Dynamic Classification plays a pivotal role in enhancing the accuracy and granularity of labeling, through providing temporal accuracy, increased granularity, reduced annotation effort through automation and enhanced ML training via more informative training data. Saving Ontology While creating your ontology, you can preview the ontology being created. The ontology can also be previewed in the JSON format. This helps you ensure that the ontology structure accurately reflects your intended categorization and that all necessary objects, classifications, and attributes are correctly defined. JSON preview offers a convenient way to validate and share the ontology's structure with team members or stakeholders. The saved ontology now can be found in the ontologies section. This ontology now can be attached to any number of annotation projects. You can edit this saved ontology at a later time; however, it's important to note that any modifications made to the ontology will also result in changes to its structure across all attached annotation projects. {{light_callout_start}} Watch the video on creating an ontology with Encord or read the documentation for more information. {{light_callout_end}} Using Ontology in Annotation Projects Now, you can apply the ontology you've created within an annotation project. Create a New Annotation Project In the left-side navigation pane, find your annotation project or create a new project.  For this demonstration, let’s create a new project. You can add a description to enhance clarity and understanding for all users on the project. Add Dataset You now have the option to either incorporate an existing dataset or generate a new dataset as needed. For detailed guidance on creating a dataset, please refer to the documentation. Add Ontology Now it's time to add the ontology you have created earlier. Here you can also create the ontology.  Set up Quality Assurance In this section, you'll be presented with a choice between two quality assurance options: Manual QA and Automated QA. In Manual QA, you can configure the percentage of annotations that require manual review. In the Automated QA mode, automated checks and validation processes are utilized by the system to evaluate annotation quality. The ontology acts as a reference and framework for these automated QA procedures, enabling validations that conform to the ontology's predefined structure. This integration not only simplifies the QA process but also guarantees consistency, precision, and adherence to project standards in annotations. For this project, you can select manual QA. Keep in mind that, once the project is created, the QA mode cannot be switched! Now Create Project! Annotation Project You can find the summary of your annotation project, the queued tasks, and who it is assigned to here. Start labeling! The highly detailed and well-structured ontology provides the capability to produce training data with a high degree of granularity. This attribute proves invaluable, especially in complex ML tasks, as it allows for the precise and detailed labeling of data.  In such projects, where discerning subtle patterns and nuances is essential, the ability to create a comprehensive dataset, underpinned by ontology, becomes crucial. This detailed dataset serves as the bedrock for training ML models, equipping them to excel in tasks that demand an in-depth comprehension of intricate concepts. Consequently, this approach leads to more robust and insightful results in complex ML applications. Usecases of Ontologies in Real-World ML Projects Now, let’s discuss real-world case studies where the integration of in-depth ontologies has not only elevated model performance but also enhanced interpretability and generalization. Medical Diagnosis and Treatment Personalization: Memorial Sloan Kettering Cancer Center Challenge Medical diagnosis is a multifaceted challenge, often requiring the consideration of various symptoms, patient history, and medical literature. Developing personalized treatment plans based on these factors demands precise understanding. Solution By constructing an ontology capturing medical concepts, symptoms, diseases, and treatment options, an ML model can be trained to comprehend the intricate relationships between these elements. The ontology not only aids in data preprocessing but also guides feature engineering by providing contextual information. Impact The model's enhanced accuracy is made possible by leveraging the intricate relationships between symptoms and diseases within the ontology, while also improving interpretability. This approach is supported by Encord's flexible ontology study, enabling: Over 1000 protocol configurations. A swift 10-minute setup time. A 100% auditable process. Seamless collaboration among team members. {{light_callout_start}} Read the Memorial Sloan Kettering Cancer Center case study here.{{light_callout_end}}  Sports Analytics with Agile Ontology Creation: Sports Tech Startup Challenge In sports analysis, it is important to detect key events, by detecting various objects and positions. Diversified data needs to be collected to build robust detection models. Solution Using an annotation tool that allowed the ML team to build new ontologies and annotate data at speed. This allowed the ML team to experiment and add new features for efficient sports analysis. Impact The flexibility of Encord's ontology-building capabilities enabled rapid experimentation, innovation, and cost-effective iteration, leading to more adventurous development and enhanced sports analytics outcomes. {{light_callout_start}} Read this case study where adopted Encord to improve their sports analysis: Rapid Annotation & Flexible Ontology for a Sports Tech Startup {{light_callout_end}}  Wrapping up In conclusion, ontologies are essential to the success of annotation projects, ensuring structured data organization and consistent labeling. Encord empowers users to create complex and nested ontologies, with Dynamic Classification adding real-time adaptability to video annotations.  Complex ontologies are invaluable in hard ML tasks, as evidenced by real-world applications in medical diagnosis and sports analytics, where they enhance accuracy and interpretability. It's a tool that paves the way for more insightful and robust results in the realm of complex machine learning applications. Ready to elevate your machine learning annotation projects with Encord's powerful ontology-driven approach? Experience the difference for yourself – Try Encord now! {{try_encord}}

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! {{light_callout_start}} 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: Slice your visual data across metrics functions to identify data slices with low performance. Flag poor-performing slices and send them for review. Export your new data set and labels. Visually explore your data through interactive embeddings, precision/recall curves, and other advanced visualizations. Check out the project on GitHub, and hey, if you like it, leave us a 🌟🫡. {{light_callout_end}} Highlights of Major Features and Changes No more streamlit: New native UI 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. Prediction import We’ve streamlined the prediction imports via the SDK. They follow the same fundamental structure, and the documentation should be clearer. 10x improvement when tagging large datasets 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. Deep Dive into Key Features Native UI 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. Direct Google Colab integration 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. Responsive Explorer interface and a button to hide annotations 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. Custom frontend components These allow for a more tailored user experience, giving you the tools and views you need without the fluff. Bug Fixes Video predictions Importing predictions for videos had a bug that assigned predictions to the wrong frames in videos (and image groups). This is now resolved. Classification predictions 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. Optimized data migrations 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. Docker file release and include `liggeos` 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.  Got rid of the ` encord-active-components` package 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. Explorer: signed URLs from AWS displayed "empty" cards 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. On Our Radar Big video projects 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 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. Filtering the “Explorer” by tags 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. What's No Longer Available? Most of the features in previous versions of Encord Active are still there. Below, we’ve listed the features that are no longer available. Export to CSV and COCO file formats Prediction confusion matrix We plan to bring back the confusion matrix, and if you’re missing the export features, please let us know in the Active community. Community Contributions 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.  {{light_callout_start}} Join our Active community for support, share your thoughts, and request features.{{light_callout_start}} Get the update now 🚀 pip install --upgrade encord-active See the releases (0.1.70 - 0.1.75) for more information Check the documentation for a quick start guide ⚠️ Remember to run `encord-active start` and not `encord-active visualize` in your project directory.