Abstract: Two-dimensional objects are displayed upon a user interface; user input selects an area and selects a machine learning model for execution. The results are displayed as an overlay over the objects in the user interface. User input selects a second model for execution; the result of this execution is displayed as a second overlay over the objects. A first overlay from a model is displayed over a set of objects in a user interface and a ground truth corresponding to the objects is displayed as a second overlay on the user interface. User input selects the ground truth overlay as a reference and causes a comparison of the first overlay with the ground truth overlay; the visual data from the comparison is displayed on the user interface. A comparison of M inference overlays with N reference overlays is performed and visual data from the comparison is displayed on the interface.

Abstract: An interactive learning cycle includes an operator, a computer and a pool of images. The operator produces a sparsely-labeled data set. A back-end system produces live feedback: a densely-labeled training set which is displayed on the computer. Immediate feedback is displayed in color on the operator computer in less than about five seconds. A labeling tool displays a user interface and for every labeling project a region is defined that is downloaded as an image data batch. The operator annotates on a per-image basis in the region and uses several UI tools to mark features in the image and group them to a predefined label class. The back-end system includes processes that run in parallel and feed back into each other, each executing a model. A local model is used independently of the global model. The global model accepts sparsely-labeled images from numerous operator computers.