Flexibench experts use rectangular box annotation to highlight objects and train data, enabling algorithms to accurately identify and localize objects during the machine
learning process.
Our annotators define each vertex of the target object, allowing for precise annotation of complex shapes. This method is ideal for use cases like aircraft detection in airports.
Flexibench annotates images at the pixel level, segmenting and labeling components within
an image. Our experts detect and classify objects to enhance image understanding in use cases like car classification.
By combining instance and semantic segmentation, Flexibench teams identify image
pixels that belong to specific classes and categorize them into distinct instances, ideal for applications like street scene analysis.
Flexibench labels 360-degree visibility images and videos captured by multi-sensor cameras, creating high-quality, ground-truth datasets for complex use cases, including those in the automotive and robotics industries.
