Binder is a free service where the webcam will not work, and performance on the video will not be good. For the best performance, install the notebooks locally.
Object detection finds predefined objects in an image or video. Each returned object includes features such as label, probability and bounding box coordinates relative to image boundaries.
List of predefined objects available in this demo: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, be, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush.
This notebook demonstrates object detection with OpenVINO, using the SSDLite MobileNetV2 model from Open Model Zoo. The model was trained on COCO dataset.
This is a self-contained example that relies solely on its own code.
We recommend running the notebook in a virtual environment. You only need a Jupyter server to start.
For details, please refer to Installation Guide.