Classification
Apply nested and higher order classes to an entire image.
- Self-driving cars
- Traffic surveillance
- Visual content moderation
Automagic Image Segmentation ToolFor Faster Image Labeling
Encord's image labeling platform helps you automate image segmentation with AI-assisted tooling, collaborative annotator management and quality assurance tools.
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Trusted by groundbreaking computer vision teams around the world
How it works
Instance and Semantic Segmentation Tool For Complex Computer Vision Projects
Encord is designed to handle advanced image segmentation projects and help you manage large annotation teams.
Encord is the only tool you need for efficient image segmentation and building active learning pipelines for advanced deep learning computer vision projects.
The Complete Image Segmentation Too
Encord provides a single platform for all image annotation needs, including:
- ✅AI-assisted segmentation
- ✅Semantic and instance segmentation
- ✅Polygon, polyline, keypoint and object primitive annotation tools
All of these features are native to the Encord platform, meaning you don’t need to use or maintain multiple custom image segmentation tools.
Annotator Management At Scale
Encord’s collaborative annotator and data management dashboards make it much easier to scale large image annotation teams, whether in-house or outsourced.
You can automatically assign annotation tasks, monitor annotators’ performance and set up custom quality control pipelines.
12x Image Annotation Speed With Automation
Auto image segmentation can help you reduce the time your annotators spend segmenting images.
Encord’s API helps you import model predictions. Our proprietary micro-models can also be used to pre-segment images so that your annotators only need to review the annotated data.
Use cases
Solve Any Image Labeling Task 10x Faster
Encord's tools support image annotation for a variety of industries such as healthcare, government, and computer vision.
Object detection
Recognise and localise objects with vector labeling tools.
- Gastroenterology
- Automated retail checkout
- Drone surveillance
Segmentation
Assign a class to each pixel of an image with segmentation masks.
- Stroke segmentation
- Pathology in microscopy
- Virtual fitting rooms
Stanford Medicine
The Division of Nephrology reduced experiment duration by 80% while processing 3x more images.
Problem
Stanford was using three different pieces of software to identify, annotate, and count podocytes and glomeruli in microscopy images.
Solution
Stanford started using Encord's annotation tools & SDK to automate segmentations, count, and calculate sizes of segments.
Results
With Encord, Stanford researchers reduced experiment duration from an average of 21 to 4 days while processing 3x the number of images.
80%
Reduction in experiment duration
3X
Number of images
1 platform
... and not 3
Complex Image Labeling Ontologies
Encord offers the industry’s most advanced labeling ontology. You can create as many nested feature types as you need to ensure your image annotation datasets provide the best ground truth for your computer vision models.
No matter how complex the image being labeled, our ontology can support the vision for your machine learning model.
Automate Your Model Development
Encord is the ideal platform for creating active learning pipelines, improving the efficiency of your projects and accelerating the development of your models.
Using Encord’s SDK and API, you can directly access projects, datasets and labels within the platform and automate annotation workflows and key features such as automated image annotation, training and inference.
Better Quality Datasets
Use Encord to identify the errors, biases and imbalances in your image annotation datasets.
Put in place granular QA workflows to ensure images are reviewed at the right level of accuracy for your use case.