The World’s Best Image Annotation Tool

A well-designed user interface can improve the user experience of the data labeling software, making it easier and more intuitive for users. When working in a well-designed user interface on a computer vision annotation tool, users apply labels consistently, using the same terminology and criteria across all data points. There's also less user bias because there are clear guidelines and instructions for labeling data. Well-designed interfaces also mean greater flexibility in terms of the types of data that can be labeled and the criteria that can be used.Overall, a well-designed UI can help improve the quality of labeled data by making the online annotation tool and process more efficient, consistent, and objective. This can in turn lead to better performance of machine learning algorithms & computer vision models that rely on labeled data.

Kili Technology natively supports multiple data formats and various formats of image files: .png, .jpeg, .gif; .bmp; .webp; .ico, and GeoTIFF for your annotation projects.

It depends: on the complexity of the task, the quality and quantity of available data, and the resources available for annotation in the visual object tagging tool. For simple tasks like object recognition or image classification, automated algorithms may be sufficient. For more complex tasks, annotating assets manually is generally considered necessary to achieve accurate results in your annotation process. The quality and quantity of available data also play a role here. If the available data is of low quality or insufficient in quantity, it may be necessary to annotate it manually to improve the labeling accuracy. Finally, if resources are limited, it may be necessary to rely on automated labeling methods or a combination of manual and automated labeling.
Kili Technology allows you to do both, making it one of the best image annotation tools.

In the traditional approach, an annotation project is created and then a large dataset is uploaded to it and annotated by humans. Then one of object detection models is trained on the labeled dataset and deployed for inference on new, unseen data. In transfer learning, a pre-trained model is used as a starting point for training a new model on a related task. The model only needs to be fine-tuned on the new dataset, which can save time and resources. You can also decide to use one of the cloud-based platforms and APIs (like Google Cloud Vision API, Amazon Rekognition, or Microsoft Azure Computer Vision) that provide access to pre-trained models and allow users to upload their own data for training and deployment.

The most common annotation image labeling tools are:


- Bounding boxes: rectangular boxes that outline an object or region of interest in the image. Used to identify and locate objects within an image.


- Semantic segmentation: each pixel in an image is labeled with a corresponding class label. Used to identify and distinguish different objects or regions of interest within an image.


- Instance segmentation: similar to semantic segmentation, but instead of just labeling pixels, each instance of an object is labeled with a unique identifier. Useful for tasks such as counting the number of objects in an image or tracking their movement over time.

Kili Technology lets you annotate images using points, lines, vectors, bounding boxes, and polygons. On top of these annotation tools, you can also use semantic segmentation or pose estimation to refine your labels further, which arguably makes Kili Technology one of the best image annotation tools.

Semi-automatic annotation interpolation is a technique used in image labeling where an algorithm is used to automatically generate annotations for some parts of an image while the user manually labels the remaining parts. The algorithm typically makes an initial guess at the labels for unannotated areas of the image based on the labels of nearby annotated areas. The user can then review and refine the automatically generated labels, correcting any errors or inconsistencies.The goal is to reduce the time and effort required for manual annotation while still achieving accurate labeling. This approach is particularly useful when annotating large datasets, where manually labeling every image may be impractical or too time-consuming.

Free labeling tools can be a good option for smaller annotation projects or for smaller budgets. They are often simple to use with user-friendly interfaces and basic annotation tools suitable for simple labeling tasks. However, they may lack advanced features and customization options and may not offer the same level of accuracy or reliability as paid image annotation tools. Also, they may not have up-to-date support or documentation. Paid labeling tools offer more advanced features, such as customizable workflows, integration with other tools or platforms, powerful smart tools like semantic segmentation, and greater accuracy and reliability. They may also have dedicated technical support and training, which can be beneficial for larger annotation projects or organizations. Additionally, paid tools may have stricter security and privacy measures in place, which can be important for sensitive or confidential data. However, the cost of these advanced labeling tools can be a barrier for some users or organizations, particularly for smaller projects or those with limited budgets.

By all means! You can use our API to add prediction or inference-type annotations generated by your pre-trained model and process your data quickly and efficiently. You can also use webhooks or Kili plugins to create and run your own code in the background (for example, to correct well-known issues automatically), thus further streamlining the whole label creation and review process. This makes Kili Technology one of the best image annotation tools.

We do support active learning. With machine learning models as part of the labeling workflow, you can expect a reduction in the number of samples to label to achieve the same performance by up to 50%. This number will depend on the dataset and the task, of course.In a demo use case with medical image classification, we experienced an increase from 78% to 85% in accuracy with the same number of samples and a 30% reduction in the number of samples needed to reach 77% accuracy. This makes Kili Technology one of the most popular image annotation tools.

It's hard to point to one specific architecture suitable for adding applications. Our customers have many different use cases for image annotation, such as identifying people in traffic cameras, cancerous cells in microscope slates, safety equipment in factory lines, and many more. A widely adopted and highly effective architecture for object detection is the Faster R-CNN (Region-based Convolutional Neural Network). It offers a good balance between accuracy and speed, making it suitable for many object detection tasks. It has been widely adopted and has achieved state-of-the-art performance on benchmarks and computer vision models like COCO (Common Objects in Context).