Quadrant Resource Data Labeling Services
Quadrant Resource LLC
Quadrant provides a comprehensive suite of Data Labeling/Annotation, Data Preparation and Model Testing services, leveraging the Data Labeling Services of the Azure ML Platform, to support our clients' machine learning or data science projects to realize their AI/ML vision. We have a proven track record in delivering high quality data labeling and annotation services to Global 100 as well as mid-market clients across several industries.
Be it a computer vision use case in support of autonomous vehicles or an NLP use case for voice recognition, we have the experience in annotating/labeling a variety of machine learning data.
Following are some examples of types of data annotation we can perform for our clients. Quadrant has successfully delivered several long-term data labeling projects involving the following types of annotation/labeling:
- Image & Video Classification & Labeling – tag a picture as a family pic, a video as a movie, label images or video segments
- Text Annotation & Transcription – Label text content; transcribe speech to text including foreign language transcription and vice-versa
- Semantic Segmentation – mark objects within a picture as people, cars etc.
- Audio/Video Segmentation – break audio, video into parts; label the segments with names of persons/speakers, type/sentiment of conversation/video and so on.
- Data Collection – collecting use-case specific video, speech, and image content
- Natural Language Processing – Validation of voice assistant accuracy, validating OCR content, validating speech to text and text to speech transcription and others
- Complex multi-step judgmental evaluation-based annotation - Complex annotation tasks that requires referring to 150–200-page instruction guides that provide specific instructions)
- LIDAR Annotation
- Annotation/Labeling Tool Experience on Bounding Boxes, Polygons, Lines, Cuboid, Point-clouds, Raster making, Painting, Super Pixeling, Label Entry, Visual Tagging, Audio/Video & Segment Marking