A self contained example demonstrating how to use MediaPipe Object Detection with Max's jweb connected to either a live webcamera stream or using still images.
This demo uses a model trained on the COCO dataset. It can identify 80 different classes of object in an image.
| 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 | bed |
| dining table | toilet | tv | laptop |
| mouse | remote | keyboard | cell phone |
| microwave | oven | toaster | sink |
| refrigerator | book | clock | vase |
| scissors | teddy bear | hair drier | toothbrush |
Still images seem to work best when objects are not too far from the camera.
This example is inspired by an example by Rob Ramirez, which is in turn inspired by MediaPipe in JavaScript.