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Fast & Precise Image Annotation Software
Our image annotation tool helps you assign annotations to images, either classification and advanced object detection. Our platform is designed to increase labeling productivity and efficiency, all in the goal to create efficient training datasets. We cover all tasks such as image classification on single class, multiclass, dropdown for a long list, and hierarchical classification to manage complex ontologies. The point, polyline, polygon, bounding box, segmentation & annotations tools make labeling data as easy as possible on images or screenshots such as png or jpg.Request a demo
Collaborative Image Annotation Platform
Kili Technology's image labelling software is available on the cloud or on-premises, and facilitates collaboration between business experts, external workforce and data-scientists on projects as users. Labelled data quality control is built-in thanks to simple & powerful tools and features: consensus analysis, honeypot, review, and many more. All is available through our application with simple interface and for all formats.Request a demo
A Powerful Image Annotation Platform
Kili Technology is designed as solution open to the ecosystem: our Python API makes Kili Technology easily integrable into your stacks. You can plug in Yolo (open source framework) to do transfer learning in order to speed up the annotation process, or Snorkel to implement weakly supervised learning strategies, which allows you to combine simple approaches to build more relevant users & labelers. All synergies for your project are possible!Request a demo
"Kili's customer support is best in-class. We solve issues much faster and it has a direct impact on our performance."
"Great companies like Kili Technology, [...] have already adopted this data-centric AI approach."
"Kili is bringing added value in the management of our projects and this is quality."
"Kili enables us to improve our models’ performance and scale our AI projects as fast as our business needs."
"We are very satisfied with our collaboration with Kili. We saw a performance improvement of our model of 3.5%"