Why Appen?

For over 20 years, Appen has worked with companies around the world to improve their speech and machine learning-based solutions by providing high-quality, human-annotated data. Our industry-leading technology platform features specialized annotation tools including several machine learning assisted options to ensure the highest accuracy and throughput.

Appen’s computer vision capabilities include:

Point Cloud Labeling (LiDAR, Radar)
Merge point cloud data and video streams into one scene to be annotated. Appen labels point cloud data to help your model understand the world around your product.

Pixel Level Semantic Segmentation
Appen’s pixel level semantic segmentation solution provides pixel-level labeling for computer vision projects that need exacting annotations.

Machine Learning Assisted Video Object Tracking
Appen’s video object tracking tool leverages an ensemble machine learning model to label videos up to 100 times faster than human-only approaches. Human annotators label objects in the first frame and the model persists those annotations, following objects as they move through the video, and relying on annotators to simply tweak and amend labels instead of relabeling each object like other solutions in the marketplace.

Image and Video Data Collection
Collect large volumes of high-quality data samples to ensure your solution can accurately recognize images and video.

Relevant Search Evaluation
Providing customized programs designed to improve the accuracy and relevancy of your search results.

Image Transcription
Annotate and transcribe directly in our image transcription tool. By combining image annotation and transcription in one task, annotators are able to localize transcriptions which allows you to train a more accurate OCR model. Even better, ML assistance can be enabled which has shown to increase transcription rates by 30% vs. manual transcription alone. This feature also supports bounding boxes and ontologies.

Image Classification
Categorize images and photos at enterprise scale. You choose the ontology and our platform will make sure everything gets labeled quickly and accurately. You can classify images by quality (detecting blurry images, for example), type (like product vs. lifestyle images), content (what’s actually in the image itself), or any other judgments you need to be made on your library of images.

Object Detection
Computer vision projects often need in-image labels. Appen’s object detection solution has tooling for bounding boxes, polygons, line labels and ellipses, all with aggregation and quality controls to make sure you get the most exacting, accurate labels possible.

Object Tagging
For images where you need to identify multiple classes and multiple instances of certain objects, Appen’s object tagging solution is a great fit. With fully customizable ontologies that support hundreds of classes, you can get your images labeled to your exact specifications.

Additional resources

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In all cases, a driverless future is coming … the question that remains is when. 

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How to Create Training Data for Computer Vision Use Cases

For simple computer vision projects, such as recognizing a pattern in a group of images, publicly available image datasets will usually suffice to train your machine learning models. But for more complex CV applications, how can you get the vast amounts of training data you need to create an accurate solution? In this post, we explain training data requirements for …

Computer Vision vs. Machine Vision

Computer Vision vs. Machine Vision — What’s the Difference?

Artificial Intelligence is an umbrella term that covers several specific technologies. In this post, we will explore machine vision (MV) and computer vision (CV). They both involve the ingestion and interpretation of visual inputs, so it’s important to understand the strengths, limitations, and best use case scenarios of these overlapping technologies. Researchers began developing computer-enabled vision technologies as early as …

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