Approaches, Applications, and Best Practices using the Appen Data Annotation Platform
Human vision systems have the advantage of learning from a lifetime of experiences how to contextualize the things we see. Machine learning models, on the other hand, usually need a substantial number of real-world scenarios to learn from to be able to product reliable computer vision (CV) outputs. These examples may come in many forms:- 2D images and video (taken from an SLR or infrared camera)
- 3-D images and video (taken from a camera or scanner)
- Sensor data (taken from RADAR or LiDAR technology).
- Sometimes a mix of the above
The Data-Centric Computer Vision eBook Will Cover:
- Approaches and applications for different computer vision use cases
- Techniques and best practices for launching successful CV-based models
- Visual examples using our platform and output code
