📄️ Classification
A classification pipeline returns a scalar result per class on each InferenceResult. See SimplifiedPipeline for the shared setup and Interpreting results for the result model.
📄️ Object detection
An object-detection pipeline returns a bounding_box result per detection on each InferenceResult. See SimplifiedPipeline for the shared setup and Drawing results to draw the detections.
📄️ Segmentation
A segmentation pipeline returns a segmentation result per detected region on each InferenceResult. Each carries a classlabel, a contour, a boundingrect, and a confidence. See SimplifiedPipeline for the shared setup.
📄️ Instance segmentation
An instance-segmentation pipeline returns both a bounding_box and a segmentation on each InferenceResult. See SimplifiedPipeline for the shared setup.
📄️ Optical character recognition (OCR)
An OCR pipeline detects text regions and recognizes their content. Each detected region is a top-level InferenceResult with a boundingbox, and the recognized text is carried on its subresults. See SimplifiedPipeline for the shared setup.
📄️ Ready-to-use OCR package
This is a ready-to-use example. All authentication, models, images and example Dockerfile are included. Download it here:
📄️ Evaluating a sequence of images
A batched image tensor is evaluated in a single run, producing one ImageInferenceResult per image in input order. This extends the classification example to a sequence. See Running inference for the batching model.